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One of the main advantages of interval type-2 fuzzy logic systems is their ability to produce prediction intervals as a by-product of the type reduction process. This is especially useful for the design of interval type-2 fuzzy logic systems, where the data are corrupted by noise, in such cases; a model that provides a granular output is more appreciable. Nevertheless, the methods have been proposed in the literature to design type-2 fuzzy logic systems only focused on optimizing a final performance measure, i.e., minimizing error of crisp output of system. This paper presents a multi-objective approach to derive interval type-2 fuzzy logic system used as predictive systems, in which there are three objective functions, such as minimization of the crisp output error and interval output errors. To assess the potentiality of the approach, it has been applied to two synthetic datasets showing very promising results. The results show that the proposed multi-objective outperforms single-objective approach in terms of the crisp and interval output quality.