This article explores the application of the NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm in optimizing parameters of project cost models using sensitivity analysis. Accurate project cost prediction is crucial in construction project management as it directly influences budgeting and resource allocation. The study begins by constructing a project cost model and applying the Sobol sensitivity analysis method to identify key parameters. Monte Carlo simulation is then employed to generate a large dataset of input samples, from which the first-order and total sensitivity indices for each variable are calculated to assess their impact on the model’s output. The NSGA-II algorithm, a multi-objective optimization technique, is utilized to balance multiple objectives within the cost model. By optimizing parameter settings, the study evaluates the model’s predictive performance under various parameter combinations, including the associated time and resource consumption. The optimized model achieved a mean squared error (MSE) of 0.042, a mean absolute error (MAE) of 0.15, and a coefficient of determination (R2) of 0.928, demonstrating its high prediction accuracy and robustness. The parameter optimization not only enhances the model’s predictive power but also efficiently manages computational costs, validating the effectiveness and practical value of the proposed method.