This paper presents a new Particle Swarm Optimization based on the concept of Tabu Search. In PSO, when a particle finds a local optimal solution, all of the particles gather around the one, and cannot escape from it. On the other hand, TS can escape from the local optimal solution by moving away from the best solution at the present. The proposed Tabu List PSO (TL-PSO) is the method for combining the excellence of both PSO and TS. In this method, it stores the history of pbest in Tabu List. When a particle has a reduced searching ability, it selects a pbest of the past from the history of them, and it is applied to update. This makes each particle active, and the searching ability of swarm makes progress. Then, the proposed method is validated through numerical simulations with several functions which are well known as optimization benchmark problems comparing to the conventional PSO methods.