Multiobjective Particle Swarm Optimization & Estimation of Distribution Algorithm (MOPSO&EDA) is presented for solving multiobjective problem. During the process of optimization, half of offspring solutions are updated by the Particle Swarm Optimization (PSO) algorithm with mutation which has the ability of global search. Another half of offspring solutions are created by the Estimation of Distribution Algorithm (EDA) which has better ability of learning and local research. EDA explicitly extracts globally statistical information from the selected solutions and builds a posterior probability distribution model of promising solutions based on the extracted information. Compared with some other multiobjective algorithms, the Pareto Sets obtained by MOPSO&EDA have good convergence and diversity performance on ZDT1~ZDT3, ZDT6, ZDT61 instances, and the performance metrics of convergence and diversity on ZDT4 are moderate.