The　key　characteristic　of　multi-objective　evolutionary　algorithm　is　that　it　can　find　a　good　approximate　multiobjective　optimal　solution　set　when　solving　multi-objective　optimization　problems(MOPs).　However,　most　multi-objective　evolutionary　algorithms　perform　well　on　regular　multi-objective　optimization　problems,　but　their　performance　on　irregular　fronts　deteriorates.　In　order　to　remedy　this　issue,　this　paper　studies　the　existing　algorithms　and　proposes　a　multi-objective　evolutionary　based　on　niche　selection　to　deal　with　irregular　Pareto　fronts.　In　this　paper,　the　crowding　degree　is　calculated　by　the　niche　method　in　the　process　of　selecting　parents　when　the　non-dominated　solutions　converge　to　the　first　front,　which　improves　the　the　quality　of　offspring　solutions　and　which　is　beneficial　to　local　search.　In　addition,　niche　selection　is　adopted　into　the　process　of　environmental　selection　through　considering　the　number　and　the　location　of　the　individuals　in　its　niche　radius,　which　improve　the　diversity　of　population.　Finally,　experimental　results　on　23　benchmark　problems　including　MaF　and　IMOP　show　that　the　proposed　algorithm　exhibits　better　performance　than　the　compared　MOEAs.