Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This work thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A GA-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated using the fuzzy-supports of the linguistic terms in the large 1-itemsets and the suitability of the derived membership functions. The proposed framework thus maintains multiple populations of membership functions, with one population for one item's membership functions. The final best set of membership functions gathered from all the populations is used to effectively mine fuzzy association rules.