In hot strip rolling process, rolling schedule is a key technology which directly influences strip product quality. Rolling schedule optimization is actually a problem of load distribution. To make a better rule of the load distribution of aluminum hot tandem rolling, multi-objective optimization algorithm is used to optimize rolling schedule. Preventing slipping, power margin and minimum energy consumption are selected as the optimization objectives. To make a precision calculation of rolling schedule, an adaptive neural network which is based on classification system is applied to improve the prediction ability for the rolling force, and its on-line training system reduces the prediction errors caused by different rolling conditions. The improved differential evolution algorithm is used to search the Pareto front, and it obtains a good approximation of the Pareto-front and decreases computation time. Load distribution strategies focused on different objectives are generated from the Pareto front to meet the requirements of industrial spots. The experiment result shows the algorithm covers the front quickly and distributes well. Comparing with the original schedule, the proposed method reduces the probability of slippage and energy consumption.