To　facilitate　the　development　of　active　distribution　networks　with　high　penetration　of　large-scale　distributed　generation　(DG)　and　electric　vehicles　(EVs),　active　management　strategies　should　be　considered　at　the　planning　stage　to　implement　the　coordinated　optimal　allocations　of　DG　and　electric　vehicle　charging　stations　(EVCSs).　In　this　article,　EV　charging　load　curves　are　obtained　by　the　Monte　Carlo　simulation　method.　This　article　reduces　the　number　of　photovoltaic　outputs　and　load　scenarios　by　the　K-means++　clustering　algorithm　to　obtain　a　typical　scenario　set.　Additionally,　we　propose　a　bi-level　programming　model　for　the　coordinated　DG　and　EVCSs　planning　problem.　The　maximisation　of　annual　overall　profit　for　the　power　supply　company　is　taken　as　the　objective　function　for　the　upper　planning　level.　Then,　each　scenario　is　optimised　at　the　lower　level　by　using　active　management　strategies.　The　improved　harmonic　particle　swarm　optimisation　algorithm　is　used　to　solve　the　bi-level　model.　The　validation　results　for　the　IEEE-33　node,　PG&E-69　node　test　system　and　an　actual　regional　30-node　distribution　network　show　that　the　bi-level　programming　model　proposed　in　this　article　can　improve　the　planning　capacity　of　DG　and　EVCSs,　and　effectively　increase　the　annual　overall　profit　of　the　power　supply　company,　while　improving　environmental　and　social　welfare,　and　reducing　system　power　losses　and　voltage　shifts.　The　study　provides　a　new　perspective　on　the　distribution　network　planning　problem.