To effectively control infectious diseases, great efforts have been made using empirical and theoretical approaches to explore the age-related characteristics of various infectious diseases, such as the force of infection (FOI), the per capita rate that a susceptible from a certain age is infected. Among them, mathematical epidemic models have contributed greatly to the control of infection within population. With the relaxed assumption of impractical unlimited vaccine supply, we developed a generalized methodology based on dynamic stochastic model utilizing the flow of limited vaccine supply to the individuals between two mutually exclusive subgroups (e.g. different age groups or different risk groups) in order to examine the long term impact of different vaccination programs on overall health outcomes from the whole population. In this model, both age dependency and seasonality in FOI were explicitly considered. This study aims to answer two key questions: 1) what is the best vaccination strategy in order to attain maximal quantitative post-vaccination health benefits among two subpopulations? and 2) how different structures of age-specific FOI’s influence our selection on the vaccinated population? Most of the time, one needs to have various parameter values in the models targeting spreading of infectious diseases in populations which cannot be estimated accurately. Therefore, a series of scenarios with different FOI’s, demographic structures, vaccine efficacy and other model parameters were used in our simulation studies using this stochastic age-structured framework. In summary, our study dynamically acquired both important biomedical and mathematical implications by successfully analyzing the current mass vaccination strategy, evaluating the effect of different prevention and intervention options, and served as an accurate basis for outcome research that may facilitate further cost-effectiveness analysis.