The　quantity　of　e-waste　is　increasing　year　by　year,　and　there　are　several　recycling　companies　in　the　same　region　conducting　the　recycling　business.　It　is　urgent　to　obtain　the　effective　overall　recycling　scheduling　schemes　considering　the　business　of　different　recycling　companies.　Multi-task　differential　evolutionary　algorithm　with　dynamic　resource　allocation　(MTDE-DRA)　is　proposed　to　realize　the　rational　allocation　of　resources　and　vehicle　routing　optimization.　First,　a　task　complexity　evaluation　metric　is　designed　to　evaluate　the　complexity　of　each　task　quantitatively.　The　saturation　of　customer　distribution　volume,　the　dispersion　of　customer　geographical　location,　and　the　spaciousness　of　customer　time　windows　are　considered　in　the　complexity　metric.　Second,　a　two-stage　resource　allocation　strategy　is　proposed　to　allocate　the　appropriate　resources　for　each　task.　The　complexity　and　evolutionary　state　of　each　task　are　used　as　key　factors　for　resource　allocation　in　the　two　stages,　respectively.　Finally,　bidirectional　evolutionary　resource　adjustment　strategy　is　proposed　to　improve　the　optimization　efficiency　of　difficult　recycling　tasks.　The　similarity　matrix　with　the　source　task　not　only　constructs　the　additional　population　for　the　current　task,　but　also　realizes　the　positive　transfer　of　knowledge.　In　addition,　experimental　studies　conducted　on　Solomon＇s　dataset　validate　the　proposed　MTDE-DRA　algorithm　is　promising　in　solving　multiple　e-waste　recycling　vehicle　routing　problems　with　different　volumes　of　business.　©　2024　Elsevier　B.V.