The increasing demand of urban distribution aggravates urban congestion and environmental pollution. In view of the growing serious environmental problems, the urban distribution of electric vehicles (EVs) can alleviate the pollution caused by vehicle emissions to a certain extent. However, in practical application, this urban distribution is affected by the scarce distribution of public charging piles and the limited mileage per charge, which bring difficulties to the operation scheduling. An optimization method considering the distribution of public charging piles and carbon emissions was proposed to improve the efficiency of the urban distribution of EVs. With the minimum total distribution cost as the optimization objective, an optimization model based on the distribution of the public charging pile network and considering the restriction of mileage per charge was established. Hill-climbing algorithm was introduced in accordance with the requirements and characteristics of the optimization model to improve the standard genetic algorithm (SGA) and its local solving capability. The feasibility and validity of the model and hill-climbing genetic algorithm (HCGA) were verified by solving small-and large-scale numerical examples. Results demonstrate that the optimization model and HCGA proposed can effectively provide optimized solutions for the urban distribution of EVs. The designed HCGA has a better searching capability than SGA. For the small-scale numerical example, the average total cost of the feasible solutions obtained by HCGA is 18.2% less than that by SGA, whereas for the large-scale example, the cost is 6% less than that by SGA. This study provides references for saving on the distribution cost and guiding the urban distribution scheduling and optimization of EVs.