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A heuristic algorithm and an improved genetic hybrid algorithm are proposed to solve the rescheduling problem of flow shop with the objective of minimizing the contract penalty and storage cost. The traditional genetic algorithm is a random and adaptive optimization algorithm based on the survival of the fittest. By means of replication, crossover and mutation, the "chromosome" group represented by the solution coding is evolved from generation to generation, and finally converges to the most appropriate group, so as to obtain the optimal or satisfactory solution of the problem. But the disadvantage is that the solution depends on the initial value, and the running time is too long. In order to give full play to the advantages of the two algorithms, a heuristic algorithm and an improved genetic hybrid algorithm are proposed. Finally, the performance of the algorithm is analyzed, and the experimental results show that the algorithm runs in a short time, and is easier to approach the global optimal solution in a large data set.