The　productive　use　of　renewable　energy　and　growth　of　electric　vehicles　(EVs)　offer　opportunities　for　sustainable　development.　Renewable　energy　systems　(RESs)　are　dispatched　by　the　designed　rational　energy　management　strategy　(EMS).　However,　the　uncertainties　of　renewable　energy　generation　and　the　risk　of　peak-on-peak　like　uncontrolled　charging　loads　burden　EMS　scheduling.　This　article　proposes　a　two-stage　EMS　for　grid-connected　RES　considering　charging　of　EVs.　The　presented　microgrid　includes　photovoltaic　system,　energy　storage　battery,　base　loads,　and　EVs,　and　microgrid　control　centers　make　decisions.　Monte　Carlo　method　is　used　to　describe　EVs　charging.　The　Stage　1　of　the　EMS　is　an　EV　sequential　charging　strategy,　taking　user　convenience,　grid　load　fluctuation,　and　user　charging　cost　as　the　objective　function,　whose　weights　are　assigned　by　the　dynamic　weighting　method　and　solved　by　the　improved　particle　swarm　algorithm.　Stage　2　is　a　multi-objective　EMS　that　considers　total　system　operating　cost　and　battery　lifetime,　which　is　addressed　by　the　improved　grey　wolf　algorithm　combined　with　the　Stage　1　solution.　Compared　to　the　normal　EMS,　in　the　case　study,　the　proposed　strategy　could　improve　the　battery　lifetime　by　72.7　%,　and　decrease　the　total　system　operating　cost　by　4.8　%.　The　proposed　EMS　effectively　meets　different　application　scenarios　and　enhances　system　economic.