With a view to the rational design of selective quinoxaline derivatives, 2D and 3D-QSAR models have been developed for the prediction of anti-tubercular activities. Successful implementation of a predictive QSAR model largely depends on the selection of a preferred set of molecular descriptors that can signify the chemico-biological interaction. Genetic algorithm (GA) and simulated annealing (SA) are applied as variable selection methods for model development. 2D-QSAR modeling using GA or SA based partial least squares (GA-PLS and SA-PLS) methods identified some important topological and electrostatic descriptors as important factor for tubercular activity. Kohonen network and counter propagation artificial neural network (CP-ANN) considering GA and SA based feature selection methods have been applied for such QSAR modeling of Quinoxaline compounds. Out of a variable pool of 380 molecular descriptors, predictive QSAR models are developed for the training set and validated on the test set compounds and a comparative study of the relative effectiveness of linear and non-linear approaches has been investigated. Further analysis using 3D-QSAR technique identifies two models obtained by GA-PLS and SA-PLS methods leading to anti-tubercular activity prediction. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The results indicate that SA is a very effective variable selection approach for such 3D-QSAR modeling.