This paper introduces a novel optimization algorithm called Charged System Search (CSS), which is inspired by principles from physics and mechanics, specifically Coulomb's law and Newtonian mechanics. CSS is a multi-agent approach where each agent, known as a Charged Particle (CP), interacts based on its fitness values and separation distances. The force between particles is determined by electrostatics, while the movement quality is governed by Newtonian mechanics. CSS is particularly suitable for non-smooth or non-convex optimization problems and does not require gradient information or continuous search spaces. The efficiency of CSS is demonstrated through standard benchmark functions and engineering design problems, showing superior performance compared to other evolutionary algorithms. The paper also discusses the limitations of traditional mathematical programming methods and highlights the advantages of meta-heuristic optimization techniques, emphasizing their ability to explore promising regions in the search space efficiently.This paper introduces a novel optimization algorithm called Charged System Search (CSS), which is inspired by principles from physics and mechanics, specifically Coulomb's law and Newtonian mechanics. CSS is a multi-agent approach where each agent, known as a Charged Particle (CP), interacts based on its fitness values and separation distances. The force between particles is determined by electrostatics, while the movement quality is governed by Newtonian mechanics. CSS is particularly suitable for non-smooth or non-convex optimization problems and does not require gradient information or continuous search spaces. The efficiency of CSS is demonstrated through standard benchmark functions and engineering design problems, showing superior performance compared to other evolutionary algorithms. The paper also discusses the limitations of traditional mathematical programming methods and highlights the advantages of meta-heuristic optimization techniques, emphasizing their ability to explore promising regions in the search space efficiently.