Brain-computer interfaces (BCIs) enables direct communication between a brain and a computer by recording and analyzing a subject’s neural activity in real-time. Research in BCI that classifies motor imagery (MI) activities are common in the literature due to its importance and applicability, e.g., stroke rehabilitation. Electroencephalography (EEG) is often used as the recording technique because its non-invasive, portable and have a relatively low cost. However, an EEG recording returns a vast number of features which must be reduced to decrease the computational time and complexity of the classifier. For this purpose, feature selection is often applied. In this study, a multiobjective evolutionary algorithm (MOEA) was used as feature selection in a high spatial and temporal feature set to (1) compare pairwise combinations of different objectives, (2) evaluate the relationship between the specific objective pair and their relation to model prediction accuracy, (3) compare multiobjective optimization versus a linear combination of the individual objectives. The results show that correlation feature selection (CFS) obtained the best performance between the evaluated objectives which were also more optimized than a linear combination of the individual objectives when classified with support vector machine (SVM).