The Group technology (GT) constitutes a manufacturing philosophy that exploits similarities in product design and product processes. The information attained from the process sheets are organized in an incidence matrix with machines and parts. The goal is to assign parts to families and machine to cells, which are designed to produce a given part family such that the number of voids and exceptional elements in cells are minimized. This article considers the problem of the manufacturing cell formation, of combinatorial nature and proposes a hybrid genetic algorithm (GA) with a greedy cosntrutive method for its solution, aiming the minimizing of the inter-cell movement and maximizing the use of the machines inside a cell. Basically, the GA generates sets of machine cells, and the constructor method is applied to those cells to better assign part families to them. The k-means algorithm is also applied to refine these formations. The performance of the proposed framework, considering the efficacy of grouping and a set of GT problems available in the literature, is presented and discussed.