the Radio Access Networks guaranteed service levels and performance management are crucial factors because of limited licensed spectrum support of cognitive Mesh mobility and multimedia services. One way to enhance the performance of Radio Access Networks is by selecting appropriate network parameters and parameter optimization. Optimum network parameters can be selected by minimizing its corresponding predefined cost function with respect to key performance indicators and best proposed optimization algorithm. In our approach, enhanced Search and Optimization based Genetic Algorithm is used to optimize UMTS Soft Handover (SHO) Overhead network parameters with proposed (Window Add, Window Drop) to increase capacity and control downlink transmission power through minimizing its cost function with selection, crossover and mutation operations. Enhanced UMTS System Level Simulator with JGAP(Java Genetic Algorithm Package) and Bonn-motion mobility scenario tool is used to optimize network parameters with respect to its proposed cost function (KPI’s) and compared with other existing intelligent optimization Algorithms(Ant colony optimization, Bee Colony Optimization, Particle Swarm Optimization(PSO). Simulation results shows that the performance of UMTS long term Evolution with modified GA is almost same as Bee colony optimization and outperforms when compared with PSO and Ant colony optimization.