Carbon (C) is a trace nonmetallic element in many components of alloy steel. Its content determines the main mechanical properties, grasp the content of element C accurately and timely plays a vital step in the process and sort of alloy steel. Double Pulse Laser-Induced Breakdown Spectroscopy (DP-LIBS) is an effective method for on-line rapid analysis of elements in alloy steel. It not only can deal sample real-time and simple sample pretreatment but also can enhance the intensity of signal and extent of ablation. In order to diminish the matrix effect and raise the precision in quantitative detection of trace element C in LIBS, a modified method of multi-element and multi spectral lines was used with an artificial neural network of back-propagation (BP-ANN). Thus a way of multivariable GA-BP-ANN was created. Firstly, the spectrum of alloy steel samples collected by DP-LIBS in an argon atmosphere, the atomic spectrum at C 193.09 nm was selected as the spectral analysis line of element C. Its intensity can correlate with the content of element C. In order to offer more spectral information and raise the accuracy of quantitative analysis, fifteen characteristic analysis spectral lines of the coexisting elements Fe, Cr, Mn and Si were selected simultaneously. The content of element Fe is rich and relatively stable in samples, which can be used as a standard internal element to reduce the fluctuation of spectral lines; then through the genetic algorithm (GA) searched, the ratios of C/Fe, Cr/Fe, Mn/Fe and Si/Fe were optimized; finally, the input of the three-layer BP-ANN was the intensity ratios of the multi-spectral line pairs selected by GA, and the output was the content of element C, the multivariable GA-BP-ANN calibration was established. In order to contrast the results of predicted, the traditional calibration curve and the univariate BP-ANN calibration methods with C/Fe as input were established. Predicted element C content in alloy steel with leave one sample, compared with the conventional calibration curve and the univariate BP-ANN methods, the average relative error of the predicted samples decreased from 14.78% and 14.75% to 8.29%, the coefficient of fitting determination between the predicted content and the certified content of element C increased from 0.967 4 and 0.974 4 to 0.989 3, respectively. The results showed that the predicted content of element C by the multivariable GA-BP-ANN calibration method was closer to the real content, which proved the feasibility of this method for the LIBS quantitative analysis of element C in alloy steel.