Swarm intelligence optimization algorithms have become an important method for solving complex optimization problems in recent years. Their core idea is to simulate the collective behavior of organisms in nature, achieving a balance between global search and local exploitation. This paper proposes a novel swarm intelligence optimization algorithm—the Softshell Turtle Optimization Algorithm (STOA). This algorithm simulates the foraging, movement, and predator avoidance behaviors of softshell turtles in nature, solving complex optimization problems through a multi-stage search strategy, dynamic step size adjustment, historical trajectory perturbation, and a group cooperation mechanism. This paper provides a detailed discussion from multiple perspectives, including algorithm modeling, mathematical formula description, theoretical analysis, and application prospects, offering new design ideas for swarm intelligence algorithms.