The compositional distribution in two-component aggregative mixing of initially bidisperse particle populations can be described by a Guassian-type function, which is determined by the mixing degree χ (assessed quantitatively by the mass-normalized power density of excess component A), and the overall mass fraction ϕ (a known value from the initial feeding condition) of component A. It is known that χ will reach a steady-state value χ∞ over time (factually, after attaining the self-preserving size distribution), and χ∞ is only relevant to ϕ, namely the feeding condition. However, the dynamic evolution of χ before the attainment of a steady-state value is not exactly known. In this paper, the fast differentially-weighted Monte Carlo method for population balance modeling was used to predict the dependence of time-varied χ on initial feeding conditions through hundreds of systematically varied simulations. It is found that χ is subject to an exponential decay, largely depending on the ratio of steady-state mixing degree and its initial value (χ∞/χ0). With the explored exponential formulas for the dynamic mixing degree, it is possible to attain an optimum control on the compositional distributions during two-component aggregation processes through selecting the initial feeding parameters, and the time needed for reaching a steady-state is investigated.