A breeding-inspired computational method for solving optimization problems in agricultural systems - SIMBREED, a set of computer heuristics, was developed. It simulated and used animal breeding as a computational metaphor for finding solutions to combinatorial optimization problems such as the general diet formulation problem (DFP). The DFP was used as a representative problem to demonstrate that SIMBREED algorithms could efficiently solve complex optimization problems. Solutions to DFP were presented by mapping morphogenetic traits to DFP variables and satisfying the objective function via natural selection or selective breeding, with small chance of mutation. The DFP solution found by ADBASE, a deterministic algorithm was compared with those found by SIMBREED algorithms. The performance of two SIMBREED algorithms: simulated natural selection (EVOLVE) and simulated selective line breeding (BREED) were also compared. Results showed that SIMBREED can be used to solve complex combinatorial optimization problems such as the DFP better than a deterministic algorithm and that the simulated selective line breeding performs better than the simulated natural selection.