Multi-objective evolutionary optimization algorithms have been received much attention in recent years, since a set of Pareto optimal candidate is provided by a single run. Generally, it is required that the provided candidates of Pareto solutions cover the Pareto front widely and uniformly. To achieve this requirement, there has been proposed a lot of variants of multi-objective evolutionary algorithms including multi-objective particle swarm models. We are able to see two major differences in the previously proposed multi-objective particle swarm models, the one is a use of single external archive and depending on additional random effect to maintain particle diversity in the swarm. In this paper, we propose more natural way to apply multi-objective optimization of particle swarm, where we introduce a personal archive that stores non-dominated candidates in each particle history. By numerical examples, the proposed method is able to provide better Pareto candidates without an additional random effect on the swarm model.