In multi-objective particle swarm optimization (MOPSO) methods, selecting good local guides (the global best particle) for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions. This paper introduces the Particle angle division method as a new method for finding the global best particle for each particle of the population. The particle angle division method is implemented and is compared with adaptive grid method [7] based on the same MOPSO for different test functions. The results show our strategy can improve convergence and diversity of MOPSO largely.