For low-carbon multimodal transport networks under uncertain transportation demand and transit time conditions, considering factors such as waiting costs, time windows, and service frequency, this study explores the multi-objective optimization problem of container multimodal transport networks. Robust optimization is employed to handle fluctuations in transportation demand, Monte Carlo simulation is used to represent uncertainties in transportation time, and mixed time window constraints are introduced to construct a stochastic robust optimization model targeting total cost, total carbon emissions, and total time. To improve algorithm convergence and maintain population diversity, an improved adaptive fast non-dominated sorting genetic algorithm II integrating multiple crossover and mutation strategies is designed. To avoid risks from parameter uncertainty affecting enterprise operations, multimodal transport operators identify the most preferred "satisfactory solutions" on the Pareto frontier according to decision-maker preferences, with decision plans favoring low-carbon transport. The improved NSGA-II algorithm effectively addresses multi-objective and uncertainty challenges in freight network optimization, providing decision-makers with a comprehensive perspective on cost, time, and environmental impact under varying robustness levels, demonstrating its potential and effectiveness in solving complex practical transportation problems. In light of their internal and