This paper presents a multi-objective constraint handling method incorporating the Particle Swarm Optimization (PSO) algorithm. The proposed approach adopts a concept of Pareto domination from multi-objective optimization, and uses a few selection rules to determine particles' behaviors to guide the search direction. A goal-oriented programming concept is adopted to improve efficiency. Diversity is maintained by perturbing particles with a small probability. The simulation results on the three engineering benchmark problems demonstrate the proposed approach is highly competitive.