Electric　logistics　vehicles　(ELVs)　have　the　potential　to　significantly　reduce　pollution　and　carbon　emissions,　which　are　considered　highly　promising　for　achieving　green　logistics.　However,　the　challenges　posed　by　time-consuming　charging　processes,　indirect　carbon　emissions,　and　fluctuating　electricity　prices　hinder　the　economic　viability　of　ELVs.　To　enhance　the　competitiveness　of　ELVs　compared　to　internal　combustion　engine　vehicles,　this　study　introduces　a　coordinated　optimization　framework　that　aims　to　minimize　both　logistics　and　electricity　costs　within　an　urban　logistics　system.　In　terms　of　logistics,　a　simultaneous　loading　and　partial　charging　strategy　is　proposed　to　improve　the　logistics　efficiency　of　ELVs　by　effectively　utilizing　the　charging　time.　In　terms　of　electricity,　we　integrate　suburban　renewable　energy　sources　with　ELVs　to　reduce　indirect　carbon　emissions　resulting　from　thermal　power　generation.　Additionally,　ELVs　are　aggregated　to　participate　in　bidding　in　the　dayahead　and　real-time　electricity　markets　through　charging　scheduling.　To　address　uncertainties　related　to　electricity　prices　and　renewable　generation,　optimal　logistics　scheduling　and　electricity　dispatch　strategies　for　ELVs　are　formulated　as　a　stochastic　programming　problem.　This　problem　is　then　solved　using　a　specially　designed　matheuristic　approach.　The　effectiveness　of　the　proposed　coordinated　optimization　framework　is　confirmed　through　case　studies　on　a　large-scale　urban　logistics　system.