Discovering　communities　in　attributed　networks　is　an　important　research　topic　in　complex　network　analysis.　Community　detection　based　on　multi-objective　evolutionary　computing　(MOEA)　models　community　detection　as　a　multi-objective　optimization　problem　and　searches　the　optimal　solutions　by　simulating　the　evolution　of　a　biological　population.　However,　the　existing　multi-objective　evolutionary　algorithms　for　community　detection　faces　two　challenges:　their　encoding　schemes　are　designed　based　on　network　topology　and　neglects　the　information　in　node　attributes;　and　they　are　easy　to　fall　into　local　optimum.　In　this　article,　we　propose　a　community　detection　algorithm　empowered　by　multi-objective　evolutionary　computing,　named　ECEVO-MOEA,　which　conducts　edge　closeness　encoding　and　embedding　vector　optimization　alternately.　On　the　one　hand,　the　evolution　of　a　biological　population　is　completed　by　employing　a　new　edge　closeness　encoding　scheme　and　multiple　attribute-aware　objective　functions.　On　the　other　hand,　the　update　of　embedding　vectors　is　used　to　calculate　similarity　matrix　and　communities　to　improve　solution　quality,　avoiding　it　from　early　convergence.　Experiments　on　real　networks　demonstrate　that　ECEVO-MOEA　achieves　higher　accuracy　than　the　baseline　algorithms.