ADHD is one of the common cognitive disorder. This disorder has a great effect on the patient’s attention and focus. To study the effect of ADHD on attention and focus, EEG is one of the modality which carry the wide insights about the brain. The state-of-the-art methods of detection and treatment of ADHD suffer from the repetitive modules of inattentive targets with a lack of motivational aspect. With the introduction of serious games, engagement and motivation in task is no longer an obstacle. Serious games are specially designed for primary purposes rather than entertainment. FOCUS is one of the serious games designed for ADHD classification. The EEG signal recorded during the FOCUS gameplay shows ADHD patient have lower attention and different EEG band activity was observed in ADHD patient than Non-ADHD subjects. In this work, we have analyzed different EEG Frequencies along with some derivative frequencies that can be useful to categorize ADHD and Non-ADHD Patients. To identify the relevant features which is having higher impact on classification, feature selection technique has been used. In this work, we have proposed Logical Jaya Optimization based feature selection method for classification of ADHD Patients. The results show that, by applying the LJaya feature selection, we have achieved classification accuracy 99.46%. The study also gives the set of relevant features that helps to improve the accuracy of classifier. The results are compared with existing research on the dataset. The results shows that the pro- posed wrapper method-based Ljaya algorithm performs better than the existing results for the classification of ADHD patient.