To solve the degree-constrained spanning minimum tree (DCMST) problems with a large scale of nodes, an optimization algorithm based on grafting and pruning operator is proposed. Learning from the flower planting techniques, this paper establishes, an evolutionary computation framework containing accelerating and adjusting operators based on conventional genetic operators. The grafting and pruning are performed by a greedy strategy and gain maximization respectively. The collision caused by possible local minima is analyzed and detected, and several methods dealing with the collision are discussed. To tackle the complexity of DCMST problems, some strategies of grafting and pruning are proposed. The convergence of the proposed algorithm and the computation complexity are analyzed. For DCMST problems of Euclidean and uniform random non-Euclidean instances from 50 to 500 nodes, the experiments show that the quality and convergence rate of the proposed method are the best compared with the known results.