The machine learning algorithms exploit a given dataset in order to build an efficient predictive or descriptive model. Multi-objective evolutionary optimization assists machine learning algorithms to optimize their hyper-parameters, usually under conflicting performance objectives and selects the best model for a given task. In this paper, recent multi-objective evolutionary approaches for four major data mining and machine learning tasks, namely: (a) data preprocessing, (b) classification, (c) clustering, and (d) association rules, are surveyed.