Abstract: To address the issue of the "peak-on-peak" phenomenon in the distribution network load in residential areas caused by the unregulated charging of electric vehicles (abbr. EVs) in the future, firstly, the corresponding relationship between charging/discharging probability and the initial state of charge was introduced to establish an EV load model considering "Many- days-a-charge". Secondly, based on the load characteristics of the region, the electricity price periods were divided; a fluctuating proportion relationship between peak and valley electricity prices was established by using the "cost compensation" method; the peak-valley time-of-use electricity prices for EV charging stations in residential areas were formulated and the EV loads after price guidance were obtained. Nevertheless, relying solely on price guidance will result in concentrated charging of EVs, leading to new peak load issues. Therefore, a dual-layer optimization scheduling model was established. Meanwhile, to improve the participation of vehicle owners, an evaluation coefficient for charging/discharging participation was introduced and a reward and punishment mechanism was determined. Finally, the proposed optimization model was solved by using the particle swarm algorithm. The results show that this optimization strategy reduces the peak-valley difference in the distribution network load and ensures the interests of vehicle owners.