This article proposes a multiobjective sizing method of the retired battery integrating with the photovoltaic solar energy used for the electric vehicle charging station (EVCS) against the charging demand uncertainty. The proposed size optimization approach employs non-dominated sorting genetic algorithm II (NSGA-II) to minimize the renewable energy waste, energy purchased from the external grid, as well as the cost characterized by the net present value produced in 20 years. Especially for the remaining life prediction of retired batteries, this article leverages the calendar-life degradation model by integrating the battery cycle-life counting method. Also, in this article, the charging demand uncertainty is built as different charging patterns for various EVCS scenarios with different combinations of fast- and slow-charging demand. Furthermore, the technoeconomic attractions of retired batteries are verified by a comprehensive comparison with the new batteries. Case studies are implemented with real-world data, and the results show that under the proposed sizing method, the EVCS could achieve a 29.4% cost reduction in the long-term operation with the retired batteries.