In this paper we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is constituted by a Pareto-based multi-objective genetic algorithm that uses clustering validation measures as the objective functions. This algorithm also uses a special consensus crossover operator. The proposed algorithm can deal with datasets with different types of clusters, without the need of much expertise in cluster analysis and the application domain. Moreover, it results in a concise and stable set of partitions representing different trade-offs between two validation measures related to different clustering criteria.