Interpretable fuzzy systems are very desirable for human users to study complex systems. To meet this end, an agent based multi-objective approach is proposed to generate interpretable fuzzy systems from experimental data. The proposed approach can not only generate interpretable fuzzy rule bases, but also optimize the number and distribution of fuzzy sets. The trade-off between accuracy and interpretability of fuzzy systems derived from our agent based approach is studied on some benchmark classification problems in the literature.