Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most contemporary community detection algorithms employ single optimization criteria (e. g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). Correspondingly, a special Multi-Objective Evolutionary Algorithm (MOEA) is designed to solve the MOP and two model selection methods are proposed. The experiments in artificial and real networks show that the multi-objective community detection algorithm is able to discover more accurate community structures.