For the multi-objective scheduling problem of smart microgrids, a collaborative optimization framework based on deep reinforcement learning (DRL) and digital twins is proposed to achieve synergistic optimization of economic efficiency (cost reduction of 18%), environmental protection (carbon emissions of 0.33 kgCO₂kwh), and reliability (power supply reliability rate ≥ 99.99%). Through empirical validation with a 200 mw microgrid, the model increased renewable energy consumption by 12% and reduced frequency excursion events by 80%. The study reveals technical bottlenecks such as storage response time (200 ms) and prediction error (RMSE 12–15%) under high renewable energy integration, providing solutions for the implementation of the “Blue Book on the Development of New Power Systems” (2023).