For low dimensional classification problems we propose the novel DIOPT approach which considers the construction of a discretized feature space. Predictions for all cells in this space are obtained by means of a reference classifier and the class labels are stored in a lookup table generated by enumerating the complete space. This then leads to extremely high classification throughput as inference consists only of discretizing the relevant features and reading the class label from the lookup table index corresponding to the concatenation of the discretized feature bin indices. Since the size of the lookup table is limited due to memory constraints, the selection of optimal features and their respective discretization levels is paramount. We propose a particular supervised discretization approach striving to achieve maximal class separation of the discretized features, and further employ a purpose-built memetic algorithm to search towards the optimal selection of features and discretization levels. The inference run time and classification accuracy of DIOPT is compared to benchmark random forest and decision tree classifiers in several publicly available data sets. Orders of magnitude improvements are recorded in classification runtime with insignificant or modest degradation in classification accuracy for many of the evaluated binary classification tasks.