Multi-objective　evolutionary　algorithms　(MOEAs)　have　proven　their　effectiveness　in　solving　two　or　three　objective　problems.　However,　recent　research　shows　that　Pareto-based　MOEAs　encounter　selection　difficulties　facing　many　similar　non-dominated　solutions　in　dealing　with　many-objective　problems.　In　order　to　reduce　the　selection　pressure　and　improve　the　diversity,　we　propose　achievement　scalarizing　function　sorting　strategy　to　make　strength　Pareto　evolutionary　algorithm　suitable　for　many-objective　optimization.　In　the　proposed　algorithm,　we　adopt　density　estimation　strategy　to　redefine　a　new　fitness　value　of　a　solution,　which　can　select　solution　with　good　convergence　and　distribution.　In　addition,　a　clustering　method　is　used　to　classify　the　non-dominated　solutions,　and　then,　an　achievement　scalarizing　function　ranking　method　is　designed　to　layer　different　frontiers　and　eliminate　redundant　solutions　in　the　environment　selection　stage,　thus　ensuring　the　convergence　and　diversity　of　non-dominant　solutions.　The　performance　of　the　proposed　algorithm　is　validated　and　compared　with　some　state-of-the-art　algorithms　on　a　number　of　test　problems　with　3,　5,　8,　10　objectives.　Experimental　studies　demonstrate　that　the　proposed　algorithm　shows　very　competitive　performance.