Classification methods are carried out in several steps. The most important step is the development of classification rules based on a priori available knowledge; this is the learning phase. This phase uses either deductive or inductive learning. Inductive learning algorithms derive a set of classification rules (or standards) from a set of already classified examples. The goal of these algorithms is to produce classification rules to predict the assignment class of a new case. Among the available knowledge we can mention the choice of the initial cluster centers which is a very important factor in the final definition of clusters. For this purpose, we proposed an evolutionary algorithm EK-means based on the combination of k-means and fuzzy c-means by touching the initialization phase of the centroids. the performance of EK-means is compared with k-means according to the metrics of interclass and intraclass distances. To compare the efficiency of our optimization solution with traditional K-means, we rely on a UCI machine learning repository. The comparative study indicates a remarkable efficiency of our proposal, regardless of the type of data.