During the development of an agent-based simulation model, the model often has to be calibrated, which means adjusting the parameters such that a reference system can be reproduced. A major problem in calibrating an agent-based simulation model is the variability of the results, due to random choices made by the agents. To reduce the variability, the numbers of agents has to be increased, which in return increases the computation time of the simulation. An attempted solution to this problem consists of increasing the numbers of agents gradually. This approach is tested with two different calibration algorithm: simulated annealing and evolutionary algorithm. Different updating schedules are applied on a test model and examined in terms of their running time and their performance. It is shown that a evolutionary algorithm with an increasing agent count manages to produce similar results as a standard calibration using only half the computation time. To conclude, the best performing calibration process is used to calibrate an existing agent-based model simulating a well known past influenza epidemic.