Accuracy and interpretability are fuzzy associative classification models optimization objective, which complement and restrict each other. So far informed research only takes classification models accuracy into account, or transforms two-objective into single-objective optimization problem. Interpretabilitys optimization method is too simple. In the research field of classification model based on multi-objective optimization and fuzzy rule, most of them generate fuzzy rule according to sample datasets quantitative attribute corresponding fuzzy items permutation and combination. When there are many quantitative attribute in the dataset, evolutionary exploration space is large. So a fuzzy associative classification model based on variant apriori and multi-objective evolutionary algorithm NSGA-II (MOEA-FACM) is proposed. MOEA-FACM adopts fuzzy confirmation measure based on probabilistic dependence to assess fuzzy associative rule in order to generate good quality rule set. Then a small number of fuzzy associative rules are selected from the prescreened candidate rule set using NSGA-II. Maximization of the classification accuracy, minimization of the number of selected rules, and minimization of the total fuzzy items in antecedent of associative rule are regarded as optimization objectives. According to Pittsburgh coding approach and biased mutation operator, a number of non-dominated rule sets, and fuzzy associative classification model, with respect to these three objectives, are built, which can obtain interpretability-accuracy tradeoff. Experiment results on benchmark data sets show that compared with homogeneous classification model, the proposed model has high accuracy, better generalization ability and less number of fuzzy associative rules and total fuzzy items, and better interpretability.