The quality of every project is determined by time, cost and scope – the triple constraint. Change in one factor incontrovertibly affects the others and attempting to minimize the cost and duration may prove to be an arduous task. Therefore, a successful project necessitates effective utilization of resources. To facilitate the project planning process, an automated intelligent tool is required to generate an optimal project schedule. Research has shown that genetic algorithms (GAs) can be used as an optimization tool in the project management domain. The main objective of this research is to investigate the use of GAs to enhance the efficacy of the project planning process by aiming to resolve time and cost conflicts. The research also includes a study of the effect of genetic environment parameters (population size, crossover rate, mutation probability, number of generations) on the solution efficiency. The paper describes the design and object oriented implementation of a genetic framework for the solution of the project scheduling problem. The proposition has been implemented in the C# language using Microsoft’s dot Net platform. The test cases depict that optimal project cost and task duration are achieved as generation size increases. In conclusion, an increase in the permutations of project schedule possibilities increases the chances to achieve minimum project cost.