Zambia has one of the highest rates of cervical cancer in the world. To help policy makers make future intervention decisions regarding cervical cancer, we created a micro-simulation model to simulate cervical cancer and HIV in Zambia. Model calibration faced two major challenges: (1) Much of the available input data was on women in the United States, which do not allow calibration to align to the age-specific targets from Zambia; (2) Significant computational resources were not always available. We addressed issue one by creating age-specific calibration parameters to help better match specific targets. Issue two was addressed by using predictive models before conducting calibration simulations to discard parameter sets that are likely to produce poor results. This paper will demonstrate these two modeling strategies and show the dramatic effect they had on our ability to accurately calibrate to model targets.