This research introduces a cohesive framework that amalgamates the ideas of optimum transfer and optimal control inside genetic algorithms, aimed at enhancing methodologies for addressing intricate optimization challenges.  The proposed framework utilizes optimum transfer ideas to enhance mating and crossing processes in genetic algorithms, leveraging optimal control theory to direct the search process and modify algorithm parameters over generations.  The proposed methodology was implemented in four distinct case studies: production scheduling, transport network design, resource allocation in cloud computing systems, and investment portfolio optimization.  The findings demonstrated the superiority of the suggested methodology compared to typical genetic algorithms for solution quality, convergence speed, and the capacity to evade local optima.  An exhaustive evaluation of computational performance and efficiency was provided, along with suggestions for the methodology's application in additional domains and its prospective advancement.