Developing effective platforms for economic energy management is considered a pivotal issue in the field of electric vehicles (EVs). To implement a cost-effective energy management platform (EMP), developers must overcome two major challenges. The first challenge lies in the environmental dynamic nature, such as EV location, energy price fluctuations, storage levels, and parking availability at charging stations. This causes most traditional one-shot optimizations to fail. The second challenge pertains to the lack of regulation in EV energy exchanges. To address these challenges, we propose a cost-aware two-stage EMP based on blockchain and deep reinforcement learning (DRL), namely, TEMP. Specifically, TEMP first develops a sharding-based blockchain energy management framework, which guarantees trust, security, privacy, traceability, and accountability without the need for intermediaries. Then, considering the complex and high-dimensional environment, TEMP devises a two-stage cooperative scheduling scheme by combining ant colony optimization (ACO) with proximal policy optimization (PPO) to enhance learning effectiveness. Evaluations show that TEMP outperforms the two state-of-the-art baselines by 12.3% and 4.4% in terms of long-term profits while reducing costs by 6.7% and 2.8%, respectively. Moreover, energy transaction efficiency can be ensured when the EV number of blockchain networks is gradually increased.