Swarm intelligence, replicating the group dynamics seen in animal and insect swarms, has become an important research field. Various algorithms based on these models have been developed to address a broad array of complex optimization challenges. This chapter presents a new swarm algorithm called Social Spider Optimization (SSO) for optimization problems. The collaborative actions of social spiders are simulated in the SSO algorithm. In this approach, a group of spiders interacting with one another is emulated by the algorithm’s individuals, guided by the biological laws of a cooperative colony. Two types of search agents (spiders) are considered: males and females. Each individual, depending on their gender, is influenced by a distinct set of evolutionary operators that replicate various cooperative behaviors found within the colony. The competence and robustness of this approach are demonstrated by comparing it to other established evolutionary methods. This comparison is based on several standard benchmark functions commonly used in the evolutionary algorithm literature. The results indicate that this method exhibits strong performance in searching for a global optimum across various reference functions.