This work examined the performance of a genetic algorithm (GA) and particle swarm optimization (PSO) in parameter estimation for a yeast growth kinetic model. Fitting the model's predictions simultaneously to three replicates of the same experiment, we used the variability among replicates as a criterion to evaluate the optimization result, since it reflects the biological variability characteristic of these systems. The performance of each algorithm was studied using 12 distinct tuning settings: a) in the GA, the tuning addressed different combinations of crossover fraction, and crossover and mutation functions; b) in the PSO, three different convergence behavior types (convergent with and without oscillations and divergent) were tested and the local and global weights were varied. The best objective function values were obtained when the PSO had convergent oscillatory behavior and a local acceleration larger than the global acceleration.