In this research, an optimization technique was developed using Genetic Algorithms to optimize the schedule of construction project activities in order to minimize the total duration of the project, subjected to both precedence and resources constraints. Genetic algorithms are a family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome like data structure and apply recombination operators to these structures so as to preserve critical information. In this research, a new approach was developed in generating the populations of the genetic algorithms generations; that is the “Feasible Solutions Developer operator" This operator enables the user to create completely feasible solutions that satisfy all constraints, and this helps in getting a quick convergence toward the best solution during Genetic algorithms stages, without losing the feature of searching global maximum or minimum. Also, a new crossover operator was developed in this study; the procedure of the new crossover operator suits the scheduling problem formulation, and suit the type of the used chromosomes. Improving the Sustainability of Low-Income housing Projects (2006)