In　the　past　few　decades,　many　variations　of　multiobjective　particle　swarm　optimization　algorithm　have　been　proposed　to　balance　the　convergence　and　diversity　of　optimal　solutions.　However,　it　is　still　difficult　to　obtain　a　set　of　accurate　and　well-distributed　solutions　for　most　complicated　multiobjective　problems.　In　this　work,　we　present　a　multi-stage　multiobjective　particle　swarm　optimization　algorithm　(MSMOPSO)　based　on　the　evolutionary　information　of　population.　The　evolutionary　information　of　population　is　evaluated　by　the　particle　evolutionary　ability,　the　population　evolutionary　ability,　and　the　particle　evolutionary　efficiency.　On　the　one　hand,　the　optimization　process　is　divided　into　two　stages　according　to　the　population　evolution　information.　Then,　a　novel　leader　selection　strategy　and　a　mutation　operator　to　the　particles　are　applied　in　different　stages,　aiming　to　improve　the　convergence　performance　and　population　diversity　separately.　On　the　other　hand,　flight　parameters　including　the　inertia　weights　and　learning　factors　are　adjusted　adaptively　and　independently,　which　can　better　balance　the　global　exploration　and　local　exploitation　abilities　of　the　algorithm.　Finally,　the　proposed　algorithm　is　evaluated　on　benchmark　test　functions.　And　results　show　that　the　MSMOPSO　is　highly　competitive　for　its　obtained　approximate　Pareto　fronts　with　uniform　distribution　and　satisfactory　convergence.