This paper presents the Multi-Objective Vortex Particle Swarm Optimization MOVPSO as a strategy based on the behavior of a particle swarm using rotational and translational motions. The MOVPSO strategy is based upon the emulation of the emerging property performed by a swarm (flock), achieving a successful motion with diversity control, via collaborative, using linear and circular movements. The proposed algorithm is tested through several multi-objective optimization functions and is compared with standard Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative results show that particle swarms behave as expected. Finally, statistical analysis allows to appreciate that the MOVPSO algorithm has a favorable performance compared to traditional MOPSO algorithm.