Hybrid scheduling problem has been known widely. However, family scheduling for hybrid flowshop environment has not been studied extensively. This study focused on two-stage hybrid flowshop scheduling problem that exists in footwear industry. The sewing process and the assembly process are two-stage process considered in this study. Sewing process and the assembly process consist of parallel production lines in which one assembly line receives the material from more than one sewing line, which is called as one value stream. A constraint of available lasts exists in second stage. We proposed two methods, Variable Neighborhood Search – Iterated Greedy (VNS-IG) and Genetic Algorithm (GA), to optimize multi-objective functions. The preliminary study was carried out for each method to determine the best parameter setting for computational experiments. We employed EDD rule to compare the performances of proposed methods. Three instances were generated to test the methods for small, medium, and large problem. The result showed that both proposed methods performed much better than EDD rule for our problem. GA becomes the best method since it yielded the lowest deviation of solution for our problem. Moreover, the difference of performance between two algorithms was very significant for large problem. The result also showed that both algorithms need to be improved for large problem since the CPU time increased very significantly.

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