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Multi-label classification consists in classifying an instance into two or more classes simultaneously. It is a very challenging task present in many real-world applications, such as classification of biology, image, video, audio, and text. Recently, the interest in interpretable classification models has grown, partially as a consequence of regulations such as the General Data Protection Regulation. In this context, we propose a multi-objective evolutionary algorithm that generates multiple rule-based multi-label classification models, allowing users to choose among models that offer different compromises between predictive power and interpretability. An important contribution of this work is that different from most algorithms, which usually generate models based on lists (ordered collections) of rules, our algorithm generates models based on sets (unordered collections) of rules, increasing interpretability. Also, by employing a conflict avoidance algorithm during the rule-creation, every rule within a given model is guaranteed to be consistent with every other rule in the same model. Thus, no conflict resolution strategy is required, evolving simpler models. We conducted experiments on synthetic and real-world datasets and compared our results with state-of-the-art algorithms in terms of predictive performance (F-Score) and interpretability (model size), and demonstrate that our best models had comparable F-Score and smaller model sizes.