Seroprevalence and the seroconversion rate (SCR) are key indicators of malaria burden, particularly in low-transmission settings. Optimizing age-based sampling may improve the efficiency and precision of SCR estimation, but this has not been systematically explored in malaria seroepidemiology. We employed Monte Carlo simulations to identify optimal age-based sampling strategies under varying assumptions by modifying (1) SCR, (2) seroreversion rate (SRR), and (3) whether SRR was known or unknown. Using the reverse catalytic model and its extension, we considered two transmission scenarios: a stable SCR and a reduction in SCR at a defined change point. Realistic ranges for SCR and SRR were selected based on previous reports, assuming serological responses to Plasmodium falciparum MSP1. In stable settings, sampling older individuals improved precision in low-transmission settings, whereas younger age groups were more informative in high-transmission settings when SRR was known. When SRR was unknown, sampling more younger individuals and maintaining a balanced age distribution yielded higher precision. Under changing transmission, sampling individuals born after the change point improved estimation of post-change SCR, while balanced sampling performed best when SRR was unknown. To illustrate the practical implications of these findings, we applied the framework to published malaria serological data from Sri Lanka. The original study employed an age distribution heavily skewed toward adults, resulting in reduced precision for the post-change SCR and limited statistical power to detect a change in transmission. Our simulations demonstrate that alternative age-based sampling strategies could substantially improve both precision and power under comparable conditions. These findings highlight the importance of careful sampling design in enhancing study efficiency, particularly in settings with limited personnel or financial resources, and emphasize that age-structured sampling strategies should be thoughtfully considered to ensure adequate precision when estimating SCR from a single cross-sectional malaria serological survey.