Charging　infrastructure　planning　(CIPL)　is　key　to　popularizing　electric　vehicles　and　reducing　carbon　emissions.　CIPL　consists　of　two　subproblems:　charging　station　siting　and　charging　pile　allocation.　The　existing　methods　independently　solve　the　two　subproblems　and　ignore　their　interaction,　which　restricts　the　rationality　of　CIPL.　To　address　this　issue,　this　paper　proposes　a　dual　ant　colony　optimization　for　CIPL　(DACO-CIPL).　In　each　iteration,　under　the　guidance　of　heuristic　information　and　pheromones,　the　upper　and　lower　ant　colonies　construct　solutions　for　charging　station　siting　and　charging　pile　allocation　in　turn,　respectively.　Then,　a　global　pheromone　update　strategy　is　performed　to　update　the　pheromones　of　each　ant　colony　according　to　the　historical　best　solutions,　which　realizes　information　transmission　from　the　lower　ant　colony　to　the　upper　ant　colony.　In　addition,　whenever　the　upper　ant　colony　finishes　constructing　solutions,　a　pheromone　enhancement　strategy　is　used　to　strengthen　the　pheromones　of　the　lower　ant　colony　according　to　the　solutions　of　the　upper　ant　colony,　which　realizes　information　transmission　from　the　upper　ant　colony　to　the　lower　ant　colony.　DACO-CIPL　is　compared　with　several　algorithms　on　multiple　test　instances.　The　experimental　results　show　that　DACO-CIPL　has　superior　performance　and　more　reasonable　options　for　CIPL.