Abstarct: Abstract: The design of two-stage operational amplifiers presents significant challenges due to the trade-offs between multiple circuit characteristics, such as gain, bandwidth, phase margin, power consumption, and chip area. Traditional optimization methods often focus on a single objective, which can result in suboptimal designs that fail to fully meet the complex requirements of modern analog circuits. This thesis explores the application of Multi-Objective Particle Swarm Optimization (MOPSO) in optimizing a two-stage operational amplifier, with the goal of achieving an optimal balance between these conflicting objectives. MOPSO is employed to navigate the multi-dimensional design space, providing a comprehensive set of Pareto-optimal solutions that represent various trade-offs between critical characteristics. The optimization process considers transistor sizing, bias currents, and compensation strategies, allowing for the simultaneous improvement of key parameters without compromising overall performance. Simulation results show that the MOPSO approach effectively provides superior design solutions compared to traditional single-objective optimization methods. The optimized two-stage operational amplifier demonstrates significant improvements in performance metrics, such as unity gain bandwidth, phase margin, and power efficiency, while minimizing silicon area. This research highlights the potential of MOPSO as a powerful tool in analog circuit design, offering a robust frxamework for achieving high-performance, area-efficient designs in advanced electronic systems.