Because　the　population　distribution　is　uneven　during　the　local　search　process　of　nondominated　sorting　genetic　algorithm　II　(NSGAII),　a　multi-objective　optimization　algorithm　for　NSGAII　based　on　uniform　distribution　(NSGAII-UID)　is　proposed.　Firstly,　the　population　which　has　been　clustered　is　mapped　to　the　hyperplane　of　the　corresponding　objective　function,　then　the　diversity　of　population　is　increased.　Secondly,　in　order　to　improve　the　distribution　uniformity　of　the　solution,　the　mapping　plane　is　evenly　partitioned.　However,　when　the　distribution　condition　is　not　satisfied　in　the　corresponding　partition,　the　distribution　enhancement　module　is　activated.　At　the　same　time　the　individuals　may　be　insufficient　or　empty　in　the　piecewise　interval　during　the　calculation　process,　in　order　to　ensure　that　the　number　of　selected　individuals　in　each　interval　is　the　same,　the　local　variation　method　of　the　best　solution　is　proposed　to　get　the　missing　individuals　lastly.　The　experimental　results　show　that　the　method　ensures　that　the　population　can　jump　out　the　local　optimal　and　the　convergence　speed　can　be　improved.　And　the　distribution　and　convergence　of　this　algorithm　is　superior　to　the　other　multi-objective　optimization　algorithms.　Copyright　©　2019　Acta　Automatica　Sinica.　All　rights　reserved.