Engineering design problems often involve multiple, conflicting objectives such as minimizing weight while maximizing strength, or reducing energy consumption while enhancing performance. Traditional optimization methods struggle to handle such trade-offs effectively, especially in high-dimensional, nonlinear search spaces. Bio-inspired optimization algorithms, modeled on natural processes like evolution, swarm intelligence, and immune systems, have emerged as powerful tools for addressing multi-objective design challenges. This paper explores the application of algorithms such as Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and Differential Evolution for solving multi-objective engineering design problems. Case studies include structural optimization, thermal system design, and electronic circuit parameter tuning. Comparative results highlight that bio-inspired methods can achieve well-distributed Pareto-optimal solutions, outperforming classical approaches in both convergence speed and solution diversity. The findings demonstrate that these algorithms offer significant promise in advancing engineering design by enabling robust, scalable, and efficient optimization