It is indeed that there is a large class of interesting problems for which no reasonably fast algorithms have been developed. Many of these problems are optimization problems that arise frequently in many practical applications. For the last few decades genetic algorithm has been playing a significant role in optimization problems. It is being used in structural optimization, functional optimization, database query optimization, parametric optimization and so on. The MMKP (Multi-dimensional Multiple Choice Knapsack Problem), a complex variant of 0-1 Classical Knapsack Problem, is an NP-Hard problem that cannot be solved in polynomial time. So the thesis is to present a genetic algorithmic approach for finding near optimal solutions of the MMKP with reduced computational complexity even with better challenging performance against some recent Heuristic Solutions such as M- HEU (Modified Heuristic) and MU-HEU (Multiple Upgrade Heuristic).It is also suitable for real-time applications. This book will be helpful for those who are related with Computer Science and working with optimization problems.