Abstract: In the context of global economic integration, optimizing multi-level supply chain networks to reduce operational delays and address disruptions and demand uncertainty is crucial. Traditional methods often handle supply chain disruptions and demand uncertainty separately, leading to fragmented solutions. This study proposes a multi-objective decision-making model to address the problem. A three-level supply chain network model is established and optimized using a real-coded genetic algorithm. This algorithm combines micro-precision with macro-flexibility to minimize cost and maximize service levels in multi-objective optimization. Specifically, the model considers the randomness of supply chain disruptions, using a geometric distribution to simulate the interval time between disruptions, and dynamically optimizes through the genetic algorithm to adapt to changes in market and supply conditions. Experimental results demonstrate that, after multiple simulation verifications, the model can achieve efficient decision-making in an average of 4.9 seconds, showcasing strong robustness and adaptability. Even with various parameter changes, the model maintains its optimization performance. Compared to traditional linear programming, mixed-integer programming, and dynamic programming models, this model excels in decision-making speed, cost control, and service levels, offering a more flexible and efficient solution for addressing supply chain disruptions and demand uncertainty.