Mathematical modeling can combine a wide range of information sources and facilitate the research of scenarios that would not be feasible to evaluate empirically. We have developed a stochastic model using genetic and physiological data from over 70 published reports on aspects of fertility in dairy cows. The model simulates cow pedigree, random mating allocation, correlated breeding values and interacting phenotypic variables. It was used to generate a large (200,000 cows replicated 100 times) data set of herd records for up to 5 parities within a seasonal dairy production system. From these data, a genetic evaluation of sires based on genetic merit for lifetime reproductive success (LRS) and the impact of high-LRS (Hi-LRS) or low-LRS (Lo-LRS) sires were investigated. LRS was defined as the number of times, during her lifetime, a cow calved within the first 42 d of the calving season. The proportion of daughters which calved (calving rate) in the 2nd parity was the strongest predictor of sire genetic merit for LRS (R² = 0.81). When 2nd parity calving date was included, the power of the prediction increased substantially (R² = 0.97). Reasonable predictions could also be made from 1st parity records. A predictive model containing 1st parity records for overall calving rate, and calving rate within the first 21 d, provided a good (R² = 0.76) LRS estimation when growth rate from weaning until first estrus was also included. Comparison of simulated daughters from widespread industry use (1000 daughters/bull) of sires with high (n = 100, µ = +0.70) and low (n = 100, µ = −0.73) breeding values for LRS, indicated that 12 of the 14 underlying genetic traits were divergent between the sire lines. Phenotypically, the daughters from the Hi-LRS sires displayed first estrus 34.1 d younger than their Lo-LRS contemporaries. Hi-LRS cows calved ~15 d younger at each parity and, despite producing less milk per season (−155L) than Lo-LRS cows, produced more milk over their lifetime (+33%) owing to additional lactations before culling. In summary, this simulation model suggests that lifetime reproductive success contributes substantially to cow productivity, and can be accurately predicted at a young age.