随着全球航空业的快速发展，机场场面航空器滑行管理难度增加，如何在保障安全和提升效率的同时减少对环境的影响变得尤为重要。针对该问题，以预防滑行路径冲突为基础约束条件，以滑行时间最短和二氧化碳（carbon dioxide, CO₂）排放量最小为优化目标建立混合整数线性优化模型，并设计非支配排序遗传算法II（non-dominated sorting genetic algorithm II, NSGA-II）进行动态求解。最后，以中国某枢纽机场为算例背景，借助Python语言实现NSGA-II算法，并与商业优化求解器Gurobi进行对比。计算结果表明：航空器数量为14架次时，与优化前相比，总滑行时间减少约17.46%，CO₂排放量降低约18.35%；NSGA-II算法得到的可行解与Gurobi所求最优解间的距离为1.083%，但NSGA-II的求解时间相对减少95.0%。同时，通过多个算例测试表明，NSGA-II算法在处理大规模多目标路径优化问题时具有显著优势。所提出的优化方案可有效提升机场场面运营效率并减少CO₂排放。

With the rapid development of the global aviation industry, airport ground operations management is increasingly challenging. Ensuring safety, improving efficiency, and reducing environmental impacts constitute critical tasks. To address this, a mixed-integer linear programming model incorporating taxiway conflict prevention was developed. This model aimed to minimize taxi time and CO₂ emissions through dynamic optimization with the non-dominated sorting genetic algorithm II (NSGA-II). Implementation was conducted in Python for a major Chinese hub airport, with results compared against the commercial optimizer Gurobi. Computational findings reveal a 17.46% reduction in total taxi time and an 18.35% decrease in CO₂ emissions across 14 aircraft. The NSGA-II solution is found to be within 1.083% of Gurobi's optimal solution, while a 95.0% faster computation time is achieved. The capability of NSGA-II in handling large-scale multi-objective taxi path optimization problems is demonstrated. Operational efficiency is enhanced, and CO₂ emissions are significantly reduced by the proposed approach.