Laser-induced breakdown spectroscopy is widely used in the material detection field because of its advantages, including online noncontact measurement and non-destructive analysis. Selecting proper analytical lines is an important prerequisite for achieving a good detection effect. This study proposed a method for adaptively selecting analytical and internal standard lines from the original spectral data of LIBS based on the global optimization ability of the genetic algorithm (GA) and the local search ability of the particle swarm optimization (PSO) algorithm. We quantitatively analyzed four major non-aluminum elements (i.e., Mg, Mn, Si, and Fe) in aluminum alloys using the analytical and internal standard lines selected using this method. The mean values of the goodness of fit, root mean square error, and relative standard deviation are 0.972, 0.35%, and 3.53%, respectively. The results obtained by traversing all other analytical lines for a quantitative analysis and comparing their calibration performances show that the analytical and internal standard lines obtained by the PSO-GA search optimization are optimal analytical spectral lines under current experimental conditions.