Tobacco is a natural plant with complex compositions, the quality of tobacco leaves is directly affected by several external factors such as geographic location and growth conditions. Tobacco leaves are widely planted in China, and they cultivated in different areas, they have different styles. Different blended ratios play a decisive role in the quality of cigarettes. Thus, there is an emerging need for accurate and rapid identification of the origin of tobacco leaves. Near-infrared spectroscopy technology provides a new rapid, and convenient method to automatically evaluate tobacco areas. On this basis, we proposed the grey wolf optimizer (GWO) algorithm to optimize the performance of the support vector machine model (SVM) for the first time to identify and classify tobacco leaves from different origins. This study was conducted with 824 tobacco leaf samples from eight different origins, and 617 training set samples and 207 test set samples were obtained using Set partitioning based on joint x-y distance (SPXY). The wavelength selection methods such as Competitive adaptive reweighted sampling (CARS) and Random frog (RF) algorithms were applied to reduce spectral redundant information and screen the characteristic wavelengths in the -full spectrum of the samples, and 141 and 534 were selected from all 1 609 variables, respectively. Then they were used as the input parameters of the SVM classifier. The optimization effect of GWO on the SVM model was contrasted to the Particle swarm optimization (PSO) and Genetic algorithm (GA) optimization in the same search range. The analysis showed that the spectral variables screened by RF had a better modeling performance than CARS. Among them, the RF-GWO-SVM model achieved the best predictive performance with an accuracy of 96.62% in identifying tobacco leaves from 8 producing areas. More than that, the running time of RF-GWO-SVM was 156 and 131 min shorter than RF-PSO-SVM and RF-GA-SVM, respectively. To sum up, RF-GWO-SVM has the advantages of higher accuracy and faster convergence speed. It can be seen that GWO has a more efficient optimization capability for model parameters, and the support vector machine model optimized by GWO can be used for rapid identification of tobacco origin.