This paper presents a multi-objective optimization scheduling model for microgrids in grid-connected mode, aiming to minimize both operational and environmental costs while maximizing efficiency. The model incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation. The improved PSO algorithm is used to solve the optimization model, and simulation results demonstrate its effectiveness in reducing electricity costs and environmental pollution. The model's performance is superior to the traditional PSO algorithm, achieving a total cost of CNY 836.23, a decrease from the pre-improvement optimal value of CNY 850. The paper also discusses the operational characteristics of various distributed energy sources and the constraints of microgrid scheduling, highlighting the importance of balancing efficiency and environmental protection. The improved PSO algorithm's enhanced convergence speed and accuracy are validated through case studies and comparisons with traditional PSO and other optimization algorithms.This paper presents a multi-objective optimization scheduling model for microgrids in grid-connected mode, aiming to minimize both operational and environmental costs while maximizing efficiency. The model incorporates improvements to the traditional particle swarm optimization (PSO) algorithm by considering inertia factors and particle adaptive mutation. The improved PSO algorithm is used to solve the optimization model, and simulation results demonstrate its effectiveness in reducing electricity costs and environmental pollution. The model's performance is superior to the traditional PSO algorithm, achieving a total cost of CNY 836.23, a decrease from the pre-improvement optimal value of CNY 850. The paper also discusses the operational characteristics of various distributed energy sources and the constraints of microgrid scheduling, highlighting the importance of balancing efficiency and environmental protection. The improved PSO algorithm's enhanced convergence speed and accuracy are validated through case studies and comparisons with traditional PSO and other optimization algorithms.