To deal with the problems of the way for selecting the global best position and reserve the diversity, a multi-objective particle swarm optimization algorithm based on decomposition and crowding distance was proposed. We introduced the Tchebycheff decompostion mechnisam and choose the best solution which comes form the neighbour weight vectors to be this particle's global best solution. To confine the flying of the particle,this paper introduced a new speed restriction factor. Comparing with three state-of-the-art multi-objective optimizers on ten test Problems, Smoeadpso outperforms the other algorithms as regards the coverage and approximation to the real pareto front.Meanwhile, the uniformity of the solution set to the 3 objective problems performs better than other particle algorithms.