When solving the optimal solutions of 0-1 Knapsack Problems (KPs), the traditional Genetic Algorithm (GA) has insufficient local refinement ability and the simple local search algorithm has limited global exploration ability. Aiming at these problems, two algorithms were integrated to the Hybrid Greedy Genetic Algorithm (HGGA). Under the GA global search framework, local search module was added, and the traditional repair operator based only on item value density was improved, the greedy hybrid option based on item value was added, so as to accelerate the optimization process. In HGGA, the population was led to carry out fine search in the excellent solution space of evolution, and the classical operators of GA were relied on to expand the global search space, so as to achieve a good balance between the refinement ability and the development ability of the algorithm. HGGA was tested on three sets of data. The results show that in the first set of 15 test cases, HGGA is able to find the optimal solution on 12 cases, with a success rate of 80%; on the second small-scale dataset, the performance of HGGA is obviously better than those of other similar GA and other meta-heuristic algorithms; on the third large-scale dataset, HGGA is more stable and efficient than other meta-heuristic algorithms.