Frequently studied structural property of networks is community structure which is described as a group of users. User interactions inside the group are more than those outside the group. Communities in networks may be overlapped as users belong to multiple groups at once. This paper proposes a new socially inspired metaheuristic search and optimization algorithm, Parliamentary Optimization Algorithm (POA), to acquire promising solutions to overlapping community detection problems considering multiple objectives. The salient and unique feature of this work is that for the first time POA has been designed as a multi-objective search method for overlapping community detection. There is not any work about multi-objective overlapping community detection problem in the related literature. For this reason, simulation results of the proposed algorithm have not been compared with any results of works. The experimental studies on both artificial and real world social networks indicate that the POA ensures beneficial results for defining multi-objective overlapping community structure. A novel and interesting application area of POA has been introduced with this work. Parallel and distributed versions of social based POA with optimized parameters may also be efficiently designed and used for different social network problems.