Particle Swarm Optimizers (PSOs) have been applied to solve Multi-Objective Optimization Problems (MOPs) for its successful applications in solving single objective optimization problems and are named as Multi-Objective PSOs (MOPSOs). However, MOPSOs are often trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A cooperative coevolutionary and ε-dominance based MOPSO (CEPSO) is proposed to attack the above disadvantages. In CEPSO, the MOPs are decomposed according to their decision variables and are optimized by corresponding subswarms respectively. Uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share one archive based on ε-dominance, which is also used as leader set. Collaborators are selected randomly from archive and used to construct context vector in order to evaluate particles in subswarm. CEPSO is tested on several classical MOP benchmark functions and the simulation results show that CEPSO can escape from local optima, optimize high dimension problems and generate more Pareto solutions. Therefore, CEPSO is competitive in solving MOPs.