This　study　addresses　a　new　electric　vehicle　routing　problem　with　time　windows　and　recharging　strategies　(EVRPTW-RS),　where　two　recharging　policies　(i.e.,　full　or　partial　recharging)　and　three　recharging　technologies　(i.e.,　normal,　rapid,　and　ultra-rapid)　are　considered.　For　this　problem,　we　first　develop　a　mixed-integer　linear　programming　model　defined　in　a　series　of　vertices　including　a　depot,　a　series　of　recharging　stations,　and　a　set　of　customers.　Due　to　the　strong　NP-hardness　of　EVRPTW-RS,　a　tailored　adaptive　large　neighborhood　search　heuristic　(ALNS)　which　contains　a　number　of　advanced　efficient　procedures　tailored　to　handle　the　proposed　problem　is　developed.　Numerical　experiments　for　benchmark　instances　generated　based　on　the　Greater　Toronto　Area　and　Ontario　in　Canada　are　conducted　to　evaluate　the　performance　of　the　proposed　model　and　ALNS.　Computational　results　demonstrate　that　the　ALNS　is　highly　effective　in　solving　EVRPTW-RS　and　outperforms　commercial　solver　CPLEX.　Moreover,　the　advantages　of　the　proposed　recharging　strategies　are　illustrated　and　some　recommendations　are　provided　for　stakeholders　when　using　electric　vehicles　for　delivery.