Engineering optimization is the process of selecting the optimal parameters to achieve performance improvement under given constraints. Optimization has a wide range of applications in different engineering fields, and accurate design boundaries and constraints to find the ideal design parameters are essential for the design of engineering projects, which can enhance the overall performance of systems and products. Whether it is component design for machinery, energy efficiency optimization for power systems, or performance improvement for software development, engineering optimization has a wide range of applications that focus on increasing efficiency and reducing costs. Engineering optimization problems are handled in this paper through a new meta-heuristic algorithm, the food digestion algorithm (FDA). Parameters are optimized step by step through the three-step digestion process of the food digestion algorithm, the mouth, stomach and the small intestine. The particles at the previous digestion point affect the particles at the next digestion point and influence the update of the next particle point. We selected three optimization algorithms to test and compare with the FDA algorithm chosen in this paper, and the results show that the optimization result of FDA is optimal in two engineering optimization problems.