Driven by the needs of several industrial projects on the applications of multiobjective search algorithms, we observed that user preferences must be properly incorporated into optimization objectives. However, existing algorithms usually treat all the objectives with equal priorities and do not provide a mechanism to reflect user preferences. To address this, we propose an extension-user-preference multiobjective optimization algorithm (UPMOA), to the most commonly applied, nondominated sorting genetic algorithm II by introducing a user preference indicator δ, based on existing weight assignment strategies [e.g., uniformly distributed weights (UDW)]. We empirically evaluated UPMOA using four industrial problems from three diverse domains (i.e., communication, maritime, and subsea oil and gas). We also performed a sensitivity analysis for UPMOA with 625 algorithm parameter settings. To further assess the performance and scalability, 103 500 artificial problems were created and evaluated representing 207 sets of user preferences. Results show that the UDW strategy with UPMOA achieves the best performance and UPMOA significantly outperformed other three multiobjective search algorithms, and has the ability to solve problems with a wide range of complexity. We also observed that different parameter settings led to the varied performance of UPMOA, thus suggesting that configuring proper parameters is highly problem-specific.