This paper presents a genetic algorithm (GA) heuristic for the multidimensional knapsack problem (MKP), a well-known NP-hard problem. The MKP involves maximizing a linear objective function subject to multiple knapsack constraints. The authors incorporate a problem-specific heuristic operator into the standard genetic algorithm approach. Computational results demonstrate that the GA heuristic can find high-quality solutions for problems of various characteristics with minimal computational effort. The heuristic outperforms several other heuristics in terms of solution quality. The MKP has applications in various fields, including capital budgeting, processor allocation, project selection, and cutting stock problems. The paper also reviews existing exact and heuristic algorithms for the MKP, highlighting the strengths and limitations of each method.This paper presents a genetic algorithm (GA) heuristic for the multidimensional knapsack problem (MKP), a well-known NP-hard problem. The MKP involves maximizing a linear objective function subject to multiple knapsack constraints. The authors incorporate a problem-specific heuristic operator into the standard genetic algorithm approach. Computational results demonstrate that the GA heuristic can find high-quality solutions for problems of various characteristics with minimal computational effort. The heuristic outperforms several other heuristics in terms of solution quality. The MKP has applications in various fields, including capital budgeting, processor allocation, project selection, and cutting stock problems. The paper also reviews existing exact and heuristic algorithms for the MKP, highlighting the strengths and limitations of each method.