A velocity-free multi-objective particle swarm optimizer with centroid is proposed and applied to optimization of wireless sensor network. Different from the standard PSO, particles in swarm only have position without velocity in the algorithm. Besides, not only the personal best position and the global best position but also the centroid is considered to update the particle position. The initial swarm is generated using the opposition-based learning, and an archive with maximum capacity is used to maintain the non-dominated solutions. The global best solution is selected from the archive on the basis of the diversity of the solutions, and the crowding-distance measure is used for the diversity measurement. The archive gets updated with the inclusion of the non-dominated solutions from the combined population of the swarm and current archive, and the archive which exceeds the maximum capacity is cut using the diversity consideration. The proposed algorithm is applied to some well-known benchmark and optimization of wireless sensor network by maximizing network coverage and lifetime. The relative experimental results show that the algorithm has better performance and is effective.