Electric vehicles (EVs) are becoming increasingly popular as a cleaner alternative to traditional gasoline-powered vehicles due to advances in battery technology, climate change impacts, and a host of other factors. However, one of the major challenges to the widespread adoption of EVs is the lack of sufficient charging infrastructure. In this study, we explore the use of predictive and optimization algorithms to estimate future demand and find the optimal placement and allocation of EV charging stations in a given charging network. For demand forecasting, we develop a neural network model that takes into account the location of charging stations and historical demand data to predict future EV charging demand. These predicted demands are then used to optimize the infrastructure. Specifically, we use the CMA-ES algorithm to optimize the placement and allocation of charging stations based on factors such as predicted demand, infrastructure costs, driving distance to charging stations, and available space. We compare the results of this approach to a baseline approach that allocates charging stations based on simple heuristics. Our results show that our proposed optimization algorithm effectively handles uncertainty, which can lead to significant improvements in the efficiency and effectiveness of EV infrastructure planning and help accelerate EV adoption.