Abstract: The constraint problem can be transformed to an optimization problem.Particle swarm optimization (PSO) is a new evolutionary computation technique.Although PSO possesses many attractive properties, but it lacks global search ability at the end of the run.This paper introduces a hybrid approach called the TPSO that simultaneously applies particle swarm optimization (PSO) , and tabu search (TS) to create a generally well-performing search heuristics, and combat the problem of premature convergence.In the new algorithm, we consider candidate solutions and their fitness as individuals, which based on their recent search progress, the tabu search cause each particle to reset its record of its best position, to avoid making direction and velocity decisions on the basis of outdated information.The feasibility of the proposed method is demonstrated on Solving Geometric Constraint Problems.