The problem of community detection in complex networks as a Multi-Objective clustering problem, and presented an evolutionary Multi-Objective approach to uncover community structure. The algorithm optimizes two objective functions able to identify densely connected groups of nodes having sparse inter connections. The method generates a set of network divisions at different hierarchical levels in which solutions at deeper levels, consisting of a higher number of modules, are contained in solutions having a lower number of communities. The cluster's should tune the size of the communities, has been considering as same group because, the partitioning found for this value are relevant. The number of modules is automatically determined by the better tradeoff values of the objective functions.