The　multi-objective　evolutionary　algorithm　based　on　decomposition　(MOEA/D)　is　proved　to　have　a　significant　advantage　dispose　of　multi-objective　optimization　problems　(MOPs)　since　its　introduction.　However,　initial　MOEA/D　worsen　diversity,　Furthermore,　it　is　easy　to　generate　an　inferior　solution　by　using　simulated　binary　crossover　operation.　Therefore,　an　improved　MOEA/D　with　an　enhanced　differential　evolution　(MOEA/D-EDE)　is　proposed　to　solve　above　problems.　The　newness　and　advantages　of　this　proposed　MOEA/D-EDE　include　the　following　two　aspects.　First,　several　differential　evolution　operators　are　used　to　replace　crossover　operator　in　the　original　MOEA/D.　Second,　MOEA/D-EDE　introduces　an　elite　archive　strategy,　thereby　significantly　increases　the　convergence　speed　while　ensuring　the　diversity.　Finally,　the　proposed　MOEA/D-EDE　is　studied　on　MOPs　compared　with　several　MOEA/D　variants　and　other　algorithms.　Empirical　results　display　that　MOEA/D-EDE　to　enhance　the　performance.