In this paper, for the distributed permutation flowshop scheduling problem, which is optimized to minimize the total tardiness, a problem sorting model is established, and a hybrid iterated greedy algorithm to solve the problem. Based on the characteristics of the problem, this paper proposes the minimum due date difference value (SDV) rule and three kinds of factory assignment rules. At the same time, combining with the nature of the problem, this paper proposes two methods to estimate the lower bound of the target value of the problem when the job is assigned to each factor. At the same time, combining with the nature of the problem, two methods for estimating the lower bound of the target value of the problem are proposed. First, through experimental analysis, it is determined that the SDV is used as a encoding rule, and the NEH combined with the lower bound estimation method is used as an improved heuristic algorithm to generate a higher quality initial solution. Secondly, in order to increase the diversity of solutions, a key factory removal strategy and simulated annealing mechanism are proposed. Then, the design is based on four A two-stage variable neighborhood descent search strategy for effective neighborhood operation is used to search different regions of the problem solution space in depth and detail in each generation of HIG. Finally, simulation experiments and algorithm comparison verify the effectiveness of hig in solving this problem.