Abstract: Constraint satisfaction problems (CSPs) is an important research branch in artificial intelligence. Recently, dynamic CSP is proposed as a powerful tool for solving many real-world problems on dynamic environments. As a result, several algorithms to solve dynamic CSPs are presented. Among those algorithms, local change (LC) algorithm based on solution reuse strategy is a method for solving many kinds of dynamic CSPs and efficient for flexible planning. On the basis of LC algorithm which is widely used, the tabu search strategy is integrated and a mini-conflict repair based algorithm is proposed, which is called Tabu_LC. The improved algorithm considers all the conflict variables as a whole, and then solves the sub-problems with branch and bound algorithm to find the best neighbor assignment, which improves the efficiency markedly. Furthermore, the Tabu_LC algorithm is implemented in the framework of constraint solving system “Ming-yue 1.0”, and compared with the LC algorithm using large amount of random CSPs. The experiment indicates that the improved algorithm has overwhelmed the LC algorithm on both the efficiency and quality of solutions.