Soluble solids content (SSC) is the main internal quality index of navel orange, in order to detect the SSC of navel orange by the combination of visible/near infrared spectroscopy and variable selection method, 141 samples of calibration set and 47 samples of prediction set were used. The transportation speed of navel orange was 0.3 m/s. The visible/near infrared spectra of navel orange samples were collected online by a USB4000 micro spectrometer. Firstly, uninformative variable elimination (UVE) and genetic algorithm (GA) were used to prescreen the wavelength variables in the wavelength range of 650~950 nm, then competitive adaptive weighted sampling (CARS) and successive projections algorithm (SPA) were used to further screen the wavelength variables. Furthermore, partial least squares (PLS) method was used to establish the online prediction models of SSC of navel orange, and these prediction models were compared with the prediction model established using original spectra. The results indicate that,for SSC of navel orange, GA method is better than UVE method in pre screening, while CARS method is better than SPA method in variable selection. GA-CARS and GA-SPA combined variable selection method is better than the corresponding single variable selection methods CARS and SPA. GA-CARS method obtains the best results for SSC of navel orange among the above variable selection methods, with the correlation coefficients of PLS model of SSC of navel orange in calibration and prediction set of 0.933 and 0.824 respectively, and the root mean square errors of calibration and prediction set are 0.429% and 0.670%, respectively. The performance of GA-CARS-PLS model is better than that of PLS model established by original spectra, and the number of modeling wavelength variables reduces from 1 385 to 78, only accounting for 5.63% of the number of original wavelength variables. In conclusion, the combined variable selection method of GA-CARS can effectively screen the wavelength variables of SSC of navel orange, and improve the stability and prediction accuracy of the prediction model.