By using the density sensitive distance as the similarity measurement, an algorithm of Density Sensitive based Multi-Agent Evolutionary Clustering (DSMAEC), based on multi-agent evolution, is proposed in this paper. DSMAEC designs a new connection based encoding, and the clustering results can be obtained by the process of decoding directly. It does not require the number of clusters to be known beforehand and overcomes the dependence of the domain knowledge. Aim at solving the clustering problem, three effective evolutionary operators are designed for competition, cooperation, and self-learning of an agent. Some experiments about artificial data, UCI data, and synthetic texture images are tested. These results show that DSMAEC can confirm the number of clusters automatically, tackle the data with different structures, and satisfy the diverse clustering request.