Considering the isolated cluster problem caused by traditional software modularization methods, a new metric named Improved Modularization Quality (IMQ) was proposed and used as the fitness function of an evolutionary algorithm to eliminate isolated clusters effectively. A mathematical programming model with the goal of maximizing IMQ was developed to represent software modularization problem. In addition, an Improved Genetic Algorithm (IGA) with competition and selection mechanism similarity was designed to solve this model. Firstly, a heuristic strategy based on edge contraction was used to generate high-quality solutions. Then the solutions were implanted as seeds into the initial population. At last, the proposed IGA was employed to further improve solution quality. Comparison experimental results prove that IMQ can effectively reduce the number of isolated clusters, and IGA has stronger robustness and ability of finding better solutions than Improved Hill Climbing Algorithm (IHC) and GA based on Group Number Encoding (GNE).