Particle swarm optimization (PSO) is a stochastic multi point search algorithm. A PSO family that is applied to multi-objective optimization problems is called multi-objective PSO. The main differences between single-objective PSO and multi-objective PSO are ``archive'' and ``guide selection''. Non-dominated solutions obtained by multi-objective PSO are stored in archive and archived members are candidates to approximate the Pareto optimal front. Therefore the archived members should be close to the true Pareto front and cover the true Pareto front widely and uniformly. In order to get better solutions, it is important that how to select the guides, what parameter to be used and how to approximate the Pareto optimal front. In this paper, a guide selection method in which either a personal best or a global best is selected depending on single interested objective function among all functions is proposed. Some combinations of the proposed guide selection method with conventional guide selection methods are examined by using several well known benchmark problems. The results show that employing the proposed guide selection method leads to better coverage on and faster convergence to the Pareto optimal front.