In view of the dynamic dispersion of e-commerce logistics demand, this paper uses the historical distribution data of logistics companies to study data-driven proactive vehicle routing optimization. First, based on the classic 2E-VRP problem, a single-node/multistage 2E-VRP mathematical model is constructed. Then, a framework for solving the proactive vehicle routing optimization problem is proposed in combination with the characteristics of the proposed model, including four modules: data-driven demand forecasting methods, customer clustering methods, proactive demand quotas and replenishment strategies, and vehicle routing optimization procedure. The significant feature of the proposed solution framework is that the response to dynamic customers is proactive rather than passive. The solution is applied to the distribution practice of a large logistics company in Chongqing. The results show that the proposed method has better dynamic scene adaptability and customer response capabilities in traffic limit.