In this paper, we propose a multi-classifier coding scheme and an entropy-based diversity criterion in evolutionary multiobjective optimization algorithms for the design of fuzzy ensemble classifiers. In a multi-classifier coding scheme, an ensemble classifier is coded as an integer string. Each string is evaluated by using its accuracy and diversity. We use two accuracy criteria. One is the overall classification rate of the string as an ensemble classifier. The other is the average classification rate of component classifiers in the ensemble classifier. As a diversity criterion, we use the entropy of outputs from component classifiers in the ensemble classifier. We examine four formulations based on the above criteria through computational experiments on benchmark data sets in the UCI machine learning repository. The experimental results show the effectiveness of the multi-classifier coding scheme and the entropy-based diversity criterion.