Genetic algorithms are computer-based search methods based loosely on the natural mechanisms of selection and evolution. Specifically, these biological metaphors are used to structure an n-dimensional search space into search sub-areas or hyperplanes which are in turn searched using chromosome analogs (usually binary strings). The use of genetic operations to re-structure these chromosomes has been found to be a powerful strategy for search. This power is derived from two features of genetic algorithms: first, search is intrinsically parallel (chromosome subsets represent discrete areas of the search space, thus all subsets operating at the same time, i.e. during the same iteration, are said to be intrinsically parallel); and second, the use of genetic operations (like mutation and crossover) allows a very complete cover of the search space such that problems of locality are avoided in a very large range of problems. Genetic algorithms were used to investigate the best configuration for a crop simulation model. The problem of 'evolving' the best model required the use of a chromosome with loci corresponding to three architecturally distinct parts of a simulation model: the mathematical functions, the function parameters, and the logic or heuristics associated with the integration of the individual response mechanisms (functions) into a single system representing the entire crop. The results of this research indicate that genetic algorithms provide a methodology for the automated design and parameterization (calibration) of simulation models.