Branching heuristic algorithms play an important role in CDCL SAT solvers, and the conventional branching heuristic algorithms are more concerned with the number of conflicts when calculating the activity scores of variables and do not consider the influence of the decision level or conflict decision level. In order to improve the efficiency of solving SAT problem, we propose a dynamic reward and punishment based branching method (DRPB), which is inspired by the EVSIDS and ACIDS. Whenever a conflict occurs, the DRPB updates the activity score of variables by integrating the numbers of conflicts, decision-making level, conflict decision level, and conflict frequency of variables. We improve the Glucose version 3.0 by replacing the VSIDS algorithm with the DRPB, and conduct an empirical evaluation not only on instances of SATLIB benchmarks, but also on 2015 and 2016 SAT competitions. Experimental results show that compared with the traditional and single variable branching heuristic algorithms, the proposed strategy can reduce the size of the search tree and improve the performance of the SAT solvers by reducing the branches of the search tree and the number of Boolean constraint propagation.