A variable selection methodology based on genetic algorithm (GA) was applied in a bilinear least squares model (BLLS) with second-order advantage, in three distinct situations, for determination by HPLC-DAD of the pesticides carbaryl (CBL), methyl thiophanate (TIO), simazin (SIM) and dimethoate (DMT) and the metabolite phthalimide (PTA) in wine. The chromatographic separation was carried out using an isocratic elution with 50:50 (v/v) acetonitrile:water as mobile phase. Preprocessing methods were performed for correcting the chromatographic time shifts, baseline variation and background. The optimization by GA provided a significant reduction of the errors, where for SIM and PTA a decrease of three times the value obtained using all variables, and an improvement in the distribution of them, reducing the observed bias in the results were observed. Comparing the RMSEP of the optimized model with the uncertainty estimates of the reference values it is observed that GA can be a very useful tool in second-order models.