Multi-objective particle swarm optimization (MOPSO) is an optimization technique inspired by bird flocking, which has been steadily gaining attention from the research community because of its high convergence speed. However, faced with multi-objective problems, adaptations are needed. Deeper researches must be conducted on its key steps, such as guide selection, in order to improve its efficiency in this context. This paper proposes an modified multi-objective particle swarm optimizer named MMOPSO, for dealing with multi-objective problems. we introduce some ideas concerning the guide selection for each particle. The proposed algorithm is compared against four multi-objective evolutionary approaches based on particle swarm optimization on four benchmark problems. The numerical results show the effectiveness of the proposed MMOPSO algorithm.