The mid-infrared spectroscopy detection can be used to the determination of methanol content in methanol gasoline. The mid-infrared spectra are susceptible to external interference and yield a large amount of data. To simplify the calculation and improve the accuracy of the model, the methods of uninformative variable elimination (UVE), competitive adaptive re-weighted sampling (CARS), and genetic algorithm (GA) are used to select effective spectral bands; then, a corresponding partial least squares (PLS) model is established. Finally, the PLS, UVE-PLS, GA-PLS, and CARS-PLS models are established to explore the optimal methanol content detection model for methanol gasoline. Results show that the CARS-PLS model performs the best, with the predicted correlation coefficient and root mean square error are 0.978 and 1.177, respectively. The CARS algorithm is a very effective wavelength extraction method for the methanol content in methanol gasoline, and detection technology utilizing the mid-infrared spectrum can be applied to determining the methanol content in methanol gasoline, which can effectively simplify calculations and improve the accuracy of the model detection.