With　the　increase　of　logistics　orders,　the　crowdsourcing　delivery　with　part-time　drivers　is　an　effective　way　to　solve　urban　logistics　distribution.　The　crowdsourcing　distribution　is　the　vehicle　routing　problem　containing　service　time　windows　of　customers　and　delivery　time　windows　of　part-time　drivers.　However,　it　is　challenging　to　obtain　optimal　scheduling　schemes　for　the　vehicle　routing　problem　considering　crowdsourcing　(VRP-C).　To　address　this　constrained　optimization　problem,　an　adaptive　constrained　multi-objective　differential　evolution　(ACMODE)　algorithm　is　designed　in　this　paper　to　minimize　the　travel　distance　and　the　driver　payment.　First,　a　two-stage　initialization　method　is　designed　to　generate　solutions　with　fewer　constraint　violations　by　dual　time　windows　operation.　Second,　a　neighborhood-oriented　search　strategy　is　developed　to　guide　searching　more　feasible　regions　and　avoiding　the　premature　convergence　of　solutions.　Third,　a　fast　selection　mechanism　based　on　an　improved　non-dominated　sorting　approach　is　proposed　to　achieve　the　tradeoff　on　objectives　and　constraints,　accelerating　the　process　of　optimization.　Finally,　several　numerical　simulation　experiments　explain　that　the　proposed　ACMODE　algorithm　can　obtain　feasible　solutions　of　the　constrained　multi-objective　optimization　problem　effectively,　and　has　better　performance　than　some　state-of-art　algorithms　in　solving　VRP-C.　©　2024