In this study, we propose a novel evolutionary algorithm-based clustering method, named density-sensitive evolutionary clustering (DSEC). In DSEC, each individual is a sequence of real integer numbers representing the cluster representatives, and each data item is assigned to a cluster representative according to a novel density-sensitive dissimilarity measure which can measure the geodesic distance along the manifold. DSEC searches the optimal cluster representatives from a combinatorial optimization viewpoint using evolutionary algorithm. The experimental results on seven artificial data sets with different manifold structure show that the novel density-sensitive evolutionary clustering algorithm has the ability to identify complex non-convex clusters compared with the K-Means algorithm, a genetic algorithm-based clustering, and a modified K-Means algorithm with the density-sensitive distance metric.