A clustering method that is based on differential evolution is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar patterns. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. To illustrate its wide applicability, the proposed algorithm is then applied to synthetic, MRI and satellite images. Experimental results show that the differential evolution clustering algorithm performs very well compared to other state-of-the-art clustering algorithms in all measured criteria. Additionally, the paper presents a different formulation to the multi-objective fitness function to eliminate the need to tune objective weights. A gbest DE is also proposed with encouraging results.