This book presents a comprehensive study on the collaborative optimization planning and scheduling methods for electric vehicles (EVs) and active distribution networks, addressing the challenges posed by large-scale EV grid integration. The research systematically investigates three core areas: EV charging demand forecasting, charging facility planning, and charging/discharging scheduling strategies. Key contributions include the development of Markov-based models for predicting EV charging behavior and battery state dynamics, and a hierarchical optimization framework for the coordinated siting and sizing of charging stations and distributed generation under coupled transportation and distribution network constraints. A spatiotemporal scheduling strategy is proposed to achieve peak shaving and valley filling, alongside a master–slave game model to balance the interests of EV users and grid operators. The study further explores the operational mechanisms of EV aggregators in electricity market environments, proposing stochastic optimization methods for day-ahead bidding and real-time regulation, incorporating risk-averse strategies based on Conditional Value at Risk to manage market uncertainties. Finally, the book extends the analysis to the dynamic coupling of power and transportation systems, introducing bounded rationality into user equilibrium models and developing Stackelberg game frameworks for fast charging station pricing and aggregation. The findings provide theoretical and technical support for enhancing grid stability, economic efficiency, and the low-carbon transformation of integrated energy systems.