Supplier clustering is important for providing more important information for the buyer. This study proposes meta-heuristics based K-modes algorithm for clustering binary dataset. There are two metaheuristic methods applied in this study, namely particle swarm optimization (PSO) and genetic algorithm (GA). The meta-heuristics are applied to give better initial modes for the K-modes algorithm. In terms of similarity measurement, this study uses Jaccard measurement since the real data set consists of higher number of value zero than one. In order to validate the proposed algorithms, three benchmark datasets are employed. The experiments results and statistical results show that PSO based K- modes algorithm is better than GA based K- modes algorithm. The data set from a exisibition company in Taiwan. In model evaluation results, PSO based K- modes algorithm has the SSE lowest than GA based K- modes algorithm.

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