Multimodal　multiobjective　optimization　problems　(MMOPs)　have　attracted　wide　attention　in　recent　years.　This　kind　of　problem　is　very　challenging　since　they　need　to　locate　different　Pareto-optimal　solution　sets　(PSs)　that　correspond　to　the　same　Pareto　front.　To　resolve　it,　this　article　proposes　a　novel　multimodal　multiobjective　differential　evolution　(DE)　algorithm　with　species　conservation,　which　develops　a　new　way　of　locating　different　PSs.　Specifically,　the　proposed　algorithm　adopts　species　conservation　to　determine　different　PSs　in　known　areas,　while　it　uses　a　variant　of　DE　as　the　basic　optimizer　to　explore　new　areas.　There　are　three　operators　in　species　conservation:　1)　species　division;　2)　seed　determination;　and　3)　seed　conservation.　Species　division　mainly　partitions　the　joint　population　of　parents　and　children　into　various　species　in　the　decision　space　for　retaining　different　PSs.　Seed　determination　selects　superior　solutions　from　each　species　as　seeds　that　need　to　be　kept　in　the　next　generation.　Seed　conservation　is　to　ensure　that　all　species　seeds　are　retained　in　the　new　generation　by　substituting　no　promising　solutions　with　them,　thereby　guarantee　not　missing　some　known　areas　that　may　contain　different　PSs.　Besides,　the　DE　variant　is　utilized　to　produce　diverse　solutions　to　find　new　areas　in　the　decision　space　where　PSs　may　exist.　The　comparative　experiments　with　ten　state-of-the-art　algorithms　have　been　performed　on　the　CEC　2019　MMOPs　test　set　and　two　real-world　problems.　The　experimental　results　have　verified　that　the　proposed　algorithm　has　a　competitive　performance　for　MMOPs.