In order to find the best scheduling strategy in the operation of the micro grid, it is easy to fall into local optimal and difficult to find the global optimal solution by using traditional intelligent algorithm to solve the micro grid optimization problem, thus using a bio-inspired method named Bird Swarm Algorithm to solve a multi-objective optimization model for micro grid taking operation cost and environmental pollution as objectives. The algorithm puts forward the corresponding strategies of population renewal imitating the birds’ foraging behavior, vigilance behavior and flight behavior. It has the advantages in high efficiency as Particle Warm Optimization (PSO) and stability as Differential Evolution algorithm (DE). It shows strong global and local search ability and high robustness when compared with PSO and DE.