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This work introduces the optimized sequential pattern problem and presents a novel approach to find such patterns. All the methods described in the literature to optimize association rules employ a single objective measure, such as optimized confidence or optimized support. In this study, we propose a novel multiobjective genetic algorithm (GA) based optimization method for optimizing quantitative sequential patterns. The objective measures of are support, confidence and a parameter related to the total number of fuzzy sets in the sequence. Experimental results on a synthetic database demonstrate the effectiveness and applicability of the proposed method.