Algorithms that dynamically schedule parallel loop iterations in a shared-memory multiprocessor have been proposed to balance the processors’ workload while maintaining low scheduling overhead. However, none of the existing strategies perform well for all types of loops on all types of system architectures. We present a generalized loop scheduling algorithm that can be adjusted to match the loop characteristics to the system environment. A new method of simulation using the Genetic Algorithm is developed to determine appropriate scheduling parameters. This approach allows us to quickly choose sets of scheduling parameters for different loops executing on different systems. Stochastic simulations show that our parameterized strategies perform at least as well as the best existing algorithms for different combinations of loop iteration characteristics and system assumptions. Our generalized strategy is thus more robust than existing strategies.