To explore the optimal feature wavebands for spectral discrimination of different tree species and the ability of classifying different tree species by spectroscopy, the leaf spectra of 10 tree species in the plantation in Guangdong Province are collected by using a spectroradiometer. The Genetic Algorithm (GA) and Successive Projection Algorithm (SPA) are used to reduce the data dimension. Then, the Support Vector Machine (SVM) and Random Forest Algorithm are used to classify the tree species. The result shows that the wavebands selected by two data dimension reduction methods are mainly located in the near infrared region. GA performs better than SPA in variable selection. The model established by RF is more stable than that established by SVM in performance. The GA-RF algorithms can be used for the tree species classification based on spectral data.