To address the issue of siting for Electric EVCS （Vehicle Charging Station）， an urban charging station siting method based on spatial semantics and individual activities was proposed. First， according to the urban planning， unsupervised learning was used to cluster the Point Of Interests （POIs） out of the service radius to determine the number of new charging stations. Then， Constrained Two-Archive Evolutionary Algorithm （CTAEA） was used to solve the objective function to optimize the electric vehicle siting scheme under the constraints of maximizing the distance between stations and covering the most POIs with new charging stations. The trajectory data and POIs of taxis in the second-ring road of Chengdu were used as the experimental samples， and siting scheme with 15 charging stations was planned. Experimental results show that compared with NSGA2 （Non-dominated Sorting Genetic Algorithm 2） and SPEA2 （Strength Pareto Evolutionary Algorithm 2）， CTAEA improves 22.9 and 20.6 percentage points on POI coverage， and reduces 18.9% and 25.5% on driver’s average selected distance， which illustrates the convenience and rationality of the method in electric vehicle charging station siting.