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Heuristics are commonly used to solve spatial harvest scheduling problems. They can generate spatially and temporally feasible solutions to large problems that traditional mathematical programming techniques are unable to solve. A common complaint about heuristics is that the quality of the solutions is unknown. We compared three heuristic techniques commonly used to solve spatial harvest scheduling problems: Monte Carlo integer programming, simulated annealing, and tabu search. Five hundred solutions to four problems, which had between 3000 to 5000 0-1 integer variables, were generated with each heuristic technique. In addition to the heuristic solutions, the optimal solution value was found to each problem using integer programming. Simulated annealing found the highest solution value for three of the four planning problems, and was less than 1% from the highest objective function value in …