diff --git a/server/internal/scheduler/scheduler.go b/server/internal/scheduler/scheduler.go
index c2066a9..1d47365 100644
--- a/server/internal/scheduler/scheduler.go
+++ b/server/internal/scheduler/scheduler.go
@@ -66,6 +66,11 @@ func (s *S) Schedule(userInfo *auth.UserInfo, prevScheduledClusterID string, gpu
 		namespacesByCluster[env.ClusterID] = env.Namespace
 	}
 	s.logger.V(1).Info("Scheduling a workload", "gpuCount", gpuCount, "assignedClustersEnvs", userInfo.AssignedKubernetesEnvs)
+
+	var (
+		bestResult *SchedulingResult
+		bestScore  float64
+	)
 	for _, c := range clusters {
 		if time.Since(c.UpdatedAt) > staleThreshold {
 			s.logger.V(1).Info("Ignoring a stale cluster", "clusterID", c.ClusterID)
@@ -82,30 +87,67 @@ func (s *S) Schedule(userInfo *auth.UserInfo, prevScheduledClusterID string, gpu
 			continue
 		}
 
-		// Just pick up the first cluster that can provision GPU resources if gpuCount is > 0.
-		// Otherwise just pick up the first cluster.
-		if gpuCount > 0 {
-			var status v1.ClusterStatus
-			if err := proto.Unmarshal(c.Status, &status); err != nil {
-				return SchedulingResult{}, err
-			}
-
-			if ok, err := canProvisionGPUs(gpuCount, &status); err != nil {
-				return SchedulingResult{}, err
-			} else if !ok {
-				s.logger.V(1).Info("Ignoring a cluster that cannot provision GPUs", "clusterID", c.ClusterID)
-				continue
+		score, err := scoreCluster(c, gpuCount)
+		if err != nil {
+			return SchedulingResult{}, err
+		}
+		if !score.isFeasible {
+			s.logger.V(1).Info("Ignoring a cluster as the workload cannot be scheduled", "clusterID", c.ClusterID)
+			continue
+		}
+		if bestResult == nil || score.score > bestScore {
+			bestResult = &SchedulingResult{
+				ClusterID:   c.ClusterID,
+				ClusterName: c.Name,
+				Namespace:   ns,
 			}
+			bestScore = score.score
 		}
-		s.logger.V(1).Info("Scheduled a workload", "clusterID", c.ClusterID, "namespace", ns)
-		return SchedulingResult{
-			ClusterID:   c.ClusterID,
-			ClusterName: c.Name,
-			Namespace:   ns,
+	}
+
+	if bestResult == nil {
+		return SchedulingResult{}, fmt.Errorf("no schedulable cluster")
+	}
+
+	s.logger.V(1).Info("Scheduled a workload", "clusterID", bestResult.ClusterID, "namespace", bestResult.Namespace)
+	return *bestResult, nil
+}
+
+type schedulingScore struct {
+	isFeasible bool
+	score      float64
+}
+
+func scoreCluster(c *store.Cluster, requestedGPUs int) (schedulingScore, error) {
+	if requestedGPUs == 0 {
+		// Simply assume that the workload can run there.
+		return schedulingScore{
+			isFeasible: true,
+			score:      0,
 		}, nil
 	}
 
-	return SchedulingResult{}, fmt.Errorf("no schedulable cluster")
+	var status v1.ClusterStatus
+	if err := proto.Unmarshal(c.Status, &status); err != nil {
+		return schedulingScore{}, err
+	}
+
+	ok, err := canProvisionGPUs(requestedGPUs, &status)
+	if err != nil {
+		return schedulingScore{}, err
+	}
+
+	if !ok {
+		return schedulingScore{
+			isFeasible: false,
+		}, err
+	}
+	return schedulingScore{
+		isFeasible: true,
+		// TODO(kenji): Improve the scoring algorithm. Currently it is a simple best-fit where a capacility with the largest
+		// number of available GPUs is selected.
+		score: float64(availableGPUs(&status)),
+	}, nil
 }
 
 // canProvisionGPUs returns true if the cluster can provision GPUs.
@@ -116,15 +158,7 @@ func canProvisionGPUs(requestedGPUs int, status *v1.ClusterStatus) (bool, error)
 		// TODO(kenji): Take into resource fragmentation.
 		// TODO(kenji): Subtract the number of GPUs allocated for this scheduling (and then revert the change
 		// once dispatcher reports the updated status including the scheduled workload.
-		var allocatable int
-		for _, n := range status.GpuNodes {
-			allocatable += int(n.AllocatableCount)
-		}
-		var allocated int
-		for _, p := range status.GpuPods {
-			allocated += int(p.AllocatedCount)
-		}
-		return requestedGPUs <= allocatable-allocated, nil
+		return requestedGPUs <= availableGPUs(status), nil
 	}
 
 	for _, pr := range status.ProvisionableResources {
@@ -148,6 +182,18 @@ func canProvisionGPUs(requestedGPUs int, status *v1.ClusterStatus) (bool, error)
 	return false, nil
 }
 
+func availableGPUs(status *v1.ClusterStatus) int {
+	var allocatable int
+	for _, n := range status.GpuNodes {
+		allocatable += int(n.AllocatableCount)
+	}
+	var allocated int
+	for _, p := range status.GpuPods {
+		allocated += int(p.AllocatedCount)
+	}
+	return allocatable - allocated
+}
+
 func isAWSInstanceTypeForNvidiaGPU(instType string) (bool, error) {
 	// Get the family from the instance type.
 	l := strings.Split(instType, ".")
diff --git a/server/internal/scheduler/scheduler_test.go b/server/internal/scheduler/scheduler_test.go
index 589eb54..f5a9693 100644
--- a/server/internal/scheduler/scheduler_test.go
+++ b/server/internal/scheduler/scheduler_test.go
@@ -145,6 +145,53 @@ func TestSchedule(t *testing.T) {
 			prevClusterID: "cluster0",
 			wantErr:       true,
 		},
+		{
+			name: "two gpu clusters",
+			clusters: []*store.Cluster{
+				{
+					ClusterID: "cluster0",
+					TenantID:  tenantID,
+					Status: marshalStatus(t, &v1.ClusterStatus{
+						GpuNodes: []*v1.GpuNode{
+							{
+								ResourceName:     "nvidia.com/gpu",
+								AllocatableCount: 16,
+							},
+						},
+					}),
+				},
+				{
+					ClusterID: "cluster1",
+					TenantID:  tenantID,
+					Status: marshalStatus(t, &v1.ClusterStatus{
+						GpuNodes: []*v1.GpuNode{
+							{
+								ResourceName:     "nvidia.com/gpu",
+								AllocatableCount: 8,
+							},
+						},
+					}),
+				},
+			},
+			userInfo: &auth.UserInfo{
+				TenantID: tenantID,
+				AssignedKubernetesEnvs: []auth.AssignedKubernetesEnv{
+					{
+						ClusterID: "cluster0",
+						Namespace: "namespace0",
+					},
+					{
+						ClusterID: "cluster1",
+						Namespace: "namespace1",
+					},
+				},
+			},
+			gpuCount: 1,
+			want: SchedulingResult{
+				ClusterID: "cluster0",
+				Namespace: "namespace0",
+			},
+		},
 	}
 
 	for _, tc := range tcs {