This paper presents a genetic algorithm (GA) based heuristic for the multidimensional knapsack problem (MKP), an NP-hard problem that involves selecting items to maximize total value while satisfying multiple constraints. The heuristic incorporates problem-specific knowledge into the standard GA approach, allowing it to efficiently find high-quality solutions with minimal computational effort. The MKP can be formulated as a maximization problem with multiple constraints, where each item is either included or excluded. The paper discusses various applications of the MKP, including capital budgeting, processor and database allocation, project selection, cargo loading, and cutting stock problems. It also notes that the MKP can be viewed as a generalization of zero-one integer programming problems. The paper reviews existing literature on the MKP, highlighting that most research has focused on the simpler single-constraint version. Exact algorithms, such as branch and bound methods, have been developed for the MKP, but they are often computationally intensive. The paper also discusses heuristic algorithms, including those based on branch and bound and relaxation techniques. The authors' GA heuristic is shown to outperform other heuristics in terms of solution quality. The paper concludes that the GA approach is effective for solving the MKP, providing high-quality solutions with reasonable computational effort.This paper presents a genetic algorithm (GA) based heuristic for the multidimensional knapsack problem (MKP), an NP-hard problem that involves selecting items to maximize total value while satisfying multiple constraints. The heuristic incorporates problem-specific knowledge into the standard GA approach, allowing it to efficiently find high-quality solutions with minimal computational effort. The MKP can be formulated as a maximization problem with multiple constraints, where each item is either included or excluded. The paper discusses various applications of the MKP, including capital budgeting, processor and database allocation, project selection, cargo loading, and cutting stock problems. It also notes that the MKP can be viewed as a generalization of zero-one integer programming problems. The paper reviews existing literature on the MKP, highlighting that most research has focused on the simpler single-constraint version. Exact algorithms, such as branch and bound methods, have been developed for the MKP, but they are often computationally intensive. The paper also discusses heuristic algorithms, including those based on branch and bound and relaxation techniques. The authors' GA heuristic is shown to outperform other heuristics in terms of solution quality. The paper concludes that the GA approach is effective for solving the MKP, providing high-quality solutions with reasonable computational effort.