Dynamic　Constrained　Multiobjective　Optimization　Problems　(DCMOPs)　are　very　difficult　to　solve　because　both　of　the　objectives　and　constraints　may　change　over　time.　The　existing　approaches　for　solving　DCMOPs　mainly　develop　dynamic　response　techniques　and　constraint　handling　techniques.　But　they　do　not　focus　on　the　search　capability　of　the　static　optimizer　in　each　environment,　which　ignores　the　intrinsic　requirement　of　quickly　locating　Pareto-optimal　Front　(PF)　in　each　environment　when　solving　DCMOPs.　To　this　end,　this　paper　proposes　a　dual　-population　evolutionary　algorithm　for　solving　DCMOPs,　called　as　DpEA,　which　maintains　a　population　without　considering　constraints　(called　UP)　for　exploration　and　a　population　with　considering　constraints　(called　CP)　for　exploitation　in　each　environment.　In　each　iteration　of　a　new　environment,　UP　firstly　adopts　a　stratified　mutation　strategy　(SMS)　and　a　dominated　solution　repairment　strategy　(DSR)　to　enhance　the　exploration　ability　of　finding　promising　regions　where　the　PF　may　reside.　SMS　uses　solutions　from　different　nondominated　fronts　to　generate　offspring,　while　DSR　repairs　the　single　-optimal　variables　of　the　dominated　solutions　by　sampling　from　the　distribution　of　those　variables　of　nondominated　solutions.　Secondly,　this　paper　uses　an　adaptive　offspring　ratio　adjustment　strategy　to　control　the　offspring　number　generated　by　UP　and　CP　according　to　the　normalized　Hausdorff　distance　between　nondominated　solution　sets　from　the　two　latest　generations　of　UP.　This　strategy　is　helpful　to　balance　the　intensity　between　exploration　and　exploitation　and　thereby　ensures　efficient　search.　Experimental　results　on　CEC　2023　DCF　test　suite　show　that　DpEA　has　a　superior　performance　over　six　state　-of　-the　-art　algorithms.