<p>In the past twenty years, the rapid development of computer technology has accelerated the application of optimization algorithms in complex problems, making engineering problems that require multi-objective optimization preliminarily solved. Therefore, multi-objective optimization has become a core research field in academia and industry. Although evolutionary and metaheuristic algorithms have emerged in large numbers, many of these methods still find it difficult to accurately approximate the Pareto optimal front and to ensure the uniform distribution of solutions in the objective space. This paper proposes a novel Multi-Objective Secretary Bird Optimization Algorithm (MOSBOA), which extends the classical Secretary Bird Optimization Algorithm (SBOA) to deal with the challenges of multi-objective optimization in engineering practice. MOSBOA adopts a reference-point-guided two-layer selection mechanism to dynamically select high-quality solutions during the iteration process. A leader mechanism is introduced, which balances global exploration and local exploitation by selecting the optimal secretary bird individuals to guide the evolutionary process. An external archive mechanism is used to collect the optimal solutions discovered during the whole iteration process. MOSBOA was tested on a benchmark set including 27 multi-objective optimization problems and two classical engineering problems, and compared with seven leading swarm intelligence and evolutionary algorithms. The experimental results show that MOSBOA exhibits outstanding performance in convergence, solution diversity, and quality, achieving first place across all metrics for 18 out of the 27 benchmark problems, showing superior performance compared with existing algorithms. The proposed MOSBOA has broad application prospects in multi-objective decision-making scenarios such as engineering design and economic scheduling, and provides an effective solution for complex multi-objective optimization tasks.</p>