A novel heuristic optimization method called Charged System Search (CSS) is introduced. Based on principles from physics and mechanics, CSS utilizes Coulomb's law from electrostatics and Newtonian mechanics to guide the search process. Each agent in CSS is a charged particle (CP), which interacts with others based on their fitness values and separation distances. The resultant force is determined by electrostatic laws, while the movement quality is determined by Newtonian mechanics. CSS is suitable for non-smooth or non-convex optimization problems and does not require gradient information or continuous search spaces. The algorithm's efficiency is demonstrated through benchmark functions and engineering design problems. Comparative studies show that CSS outperforms other evolutionary algorithms. The paper discusses two general optimization methods: mathematical programming and metaheuristics. Mathematical programming methods, such as linear programming and nonlinear programming, use gradient information to find solutions but require continuous variables and a good starting point. However, they struggle with non-convex or non-smooth problems. Metaheuristics, on the other hand, are suitable for global optimization and do not require continuous functions. They are inspired by natural phenomena, such as evolutionary processes, animal behavior, and physical laws. Examples include Evolutionary Algorithms, Genetic Algorithms, Tabu Search, Ant Colony Optimization, Particle Swarm Optimization, Simulated Annealing, Big Bang–Big Crunch, and Gravitational Search Algorithm. The objective of this paper is to present CSS, a new optimization algorithm based on physics and mechanics. Section 2 describes the background and principles of CSS, including Coulomb's law and Newtonian mechanics. Section 3 presents numerical examples to verify the algorithm's efficiency, and Section 4 provides concluding remarks.A novel heuristic optimization method called Charged System Search (CSS) is introduced. Based on principles from physics and mechanics, CSS utilizes Coulomb's law from electrostatics and Newtonian mechanics to guide the search process. Each agent in CSS is a charged particle (CP), which interacts with others based on their fitness values and separation distances. The resultant force is determined by electrostatic laws, while the movement quality is determined by Newtonian mechanics. CSS is suitable for non-smooth or non-convex optimization problems and does not require gradient information or continuous search spaces. The algorithm's efficiency is demonstrated through benchmark functions and engineering design problems. Comparative studies show that CSS outperforms other evolutionary algorithms. The paper discusses two general optimization methods: mathematical programming and metaheuristics. Mathematical programming methods, such as linear programming and nonlinear programming, use gradient information to find solutions but require continuous variables and a good starting point. However, they struggle with non-convex or non-smooth problems. Metaheuristics, on the other hand, are suitable for global optimization and do not require continuous functions. They are inspired by natural phenomena, such as evolutionary processes, animal behavior, and physical laws. Examples include Evolutionary Algorithms, Genetic Algorithms, Tabu Search, Ant Colony Optimization, Particle Swarm Optimization, Simulated Annealing, Big Bang–Big Crunch, and Gravitational Search Algorithm. The objective of this paper is to present CSS, a new optimization algorithm based on physics and mechanics. Section 2 describes the background and principles of CSS, including Coulomb's law and Newtonian mechanics. Section 3 presents numerical examples to verify the algorithm's efficiency, and Section 4 provides concluding remarks.