In this paper, we propose a multi-objective genetic-fuzzy mining algorithm for extracting both membership functions and association rules from quantitative transactions. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions. It consists of two factors, coverage factor and overlap factor, to avoid two bad types of membership functions. The second one is the total number of large I-itemsets from a, given set of minimum support values. The two criteria have a trade-off relationship. Experimental results also show the effectiveness of the proposed approach in finding the Pareto-front membership functions.