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Generalized grouping problem have more than one process plans and or process routes. In non-heierarchical methods all decisions are made simultaneously and in heierchical methods decisions are made in stages. Because of complexity of the generalized problem, solving large size problems using simultaneous approach becomes difficult. The grouping problem assumes a particular structure depending on the objectives and the constraints. The mathematical models of generalized grouping are found to be either NP-complete or hard to solve. Since even the relaxed version of grouping problem is NP-complete, it is unlikely that the optimal solution to the problem can be found efficiently. Genetic Algorithm is largely used for solving problems in cellular manufacturing. In this paper, a model is developed to solve the generalized grouping problem considering alternative process plans. Several design and manufacturing parameters such as production volume, process sequence, machine capacity, processing time, machine duplication, number of cells and cell size are considered. The objective function minimizes intercellular movements and number of exceptional elements. A procedure based on genetic algorithms to solve the problem in two phases has been demonstrated. In the first phase it finds the process routes and in the next it forms grouping of machines. The algorithm coded in C++ was tested on Windows workstation. The objective to form cells and part families was based on a double grouping (operations and machines). The final solution is a proposition of machine cells defining part families. The algorithm is really fast and allows trying different configurations for the set of data and different alternatives of the weights for all criteria. 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Generalized grouping problem have more than one process plans and or process routes. In non-heierarchical methods all decisions are made simultaneously and in heierchical methods decisions are made in stages. Because of complexity of the generalized problem, solving large size problems using simultaneous approach becomes difficult. The grouping problem assumes a particular structure depending on the objectives and the constraints. The mathematical models of generalized grouping are found to be either NP-complete or hard to solve. Since even the relaxed version of grouping problem is NP-complete, it is unlikely that the optimal solution to the problem can be found efficiently. Genetic Algorithm is largely used for solving problems in cellular manufacturing. In this paper, a model is developed to solve the generalized grouping problem considering alternative process plans. Several design and manufacturing parameters such as production volume, process sequence, machine capacity, processing time, machine duplication, number of cells and cell size are considered. The objective function minimizes intercellular movements and number of exceptional elements. A procedure based on genetic algorithms to solve the problem in two phases has been demonstrated. In the first phase it finds the process routes and in the next it forms grouping of machines. The algorithm coded in C++ was tested on Windows workstation. The objective to form cells and part families was based on a double grouping (operations and machines). The final solution is a proposition of machine cells defining part families. The algorithm is really fast and allows trying different configurations for the set of data and different alternatives of the weights for all criteria. 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