Electrified-Autonomous Flexible Transit (E-AFT) represents a promising paradigm for on-demand mobility, necessitating the integration of routing and energy management to ensure viable operations. This study develops a two-stage optimization model for dynamic vehicle routing and charging scheduling, formulated as a Mixed-Integer Nonlinear Programming (MINLP) framework designed to maximize overall system profit. In the first stage, an Adaptive Large Neighborhood Search (ALNS) algorithm determines routes to maximize operation profit, with energy consumption and time constraints explicitly linking to the second stage Variable Neighborhood Search (VNS) which optimizes charging schedules to minimize total charging costs. This sequential ALNS-VNS procedure is embedded within a Rolling Horizon Control (RHC) strategy, effectively tackling the computational challenges of large-scale, real-time demand through iterative subproblem resolution. Validation using real-world urban network case studies demonstrates the model’s effectiveness: the ALNS-VNS approach achieves near-optimal solutions with superior computational efficiency, and the RHC framework reveals the significant impact of horizon interval and battery capacity on service reliability and economic feasibility, offering valuable insights for E-AFT system design.