This research paper introduces Meta Plan Optimization (MPO), a new framework to improve how large language model agents plan for tasks. MPO uses high-level, general instructions called meta plans to guide the agents, helping them avoid planning errors and the need for retraining on each new task. The framework includes a meta planner that generates these guiding plans and is refined based on feedback from the agent's task performance. Experiments on household and science tasks demonstrate that MPO significantly boosts the efficiency and success rates of various agents, even in unfamiliar situations. This approach provides a plug-and-play method for enhancing agent capabilities by optimizing the guiding meta plans through interaction and learning.