Green supply chain optimization is a crucial strategy for achieving sustainable development, as it addresses the need for environmentally friendly practices while maintaining economic efficiency. This study explores the application of intelligent management accounting as an effective optimization tool in green supply chain management. Utilizing experimental comparison methods, the research implements various algorithms, including genetic algorithm, particle swarm optimization algorithm, and ant colony optimization algorithm, to enhance green supply chain optimization. The experimental results indicate that the genetic algorithm outperforms the others, achieving lower costs, reduced CO2 emissions, and shorter transportation times. Following this, the particle swarm optimization algorithm and ant colony optimization algorithm also demonstrate significant optimization effects, though with slight variations in performance. In conclusion, the study highlights that intelligent management accounting, when integrated with these optimization algorithms, facilitates a balance between cost-effectiveness and environmental benefits in green supply chain management, contributing to a sustainable future.