Abstract: In order to overcome the local optimum and premature convergence of traditional intelligent algorithms in multi-objective optimal scheduling of microgrids,improve the accuracy,stability and convergence speed of microgrid scheduling,a multi-objective optimal scheduling strategy for microgrids based on improved bird swarm algorithm is proposed. Firstly,the influencing factors of microgrid multi-objective scheduling are analyzed,the microgrid scheduling model based on minimum economic cost and minimum environmental impact is established. Secondly,by adaptively selecting inertia weight coefficients through random uniform distribution,linear adjustment strategy and learning coefficients are applied to balance global and local search ability,improve the convergence speed and search accuracy of bird flock algorithm,and update the spatial position of bird population based on Lévy flight strategy to expand the search range and enrich the population diversity,so that the proposed method can jump out of the local optimum and achieve accurate convergence. Finally,simulation experiments are conducted by building a typical microgrid scenario under grid-connected operation conditions,and the proposed method is compared and analyzed with other mature algorithms by using typical test functions.The experimental results show that the global optimization performance,convergence accuracy,stability and convergence speed of the proposed method are better than other comparative methods,and the proposed method has good economic and environmental friendliness,and can achieve good multi-objective optimization balance.