We are interested in predicting changes in genetics and profitability of dairy herds when different reproductive strategies are combined with genetic selection. Our objective was to incorporate 12 correlated genetic traits included in Net Merit Index in a dynamic, stochastic model. An existing dynamic, stochastic model that mimics the biology and management of a herd of individual young stock and cows over time was adapted as follows. A true breeding value (TBV) for each trait was calculated as the average of the sire’s and dam’s TBV, plus a fraction of the inbreeding and Mendelian sampling. TBV were calculated from the Cholesky decomposed genetic covariance matrix of an average unselected Holstein population given as input, multiplied with by a 12 × 1 matrix of standard normal deviates. Similarly, an environmental component for each trait was calculated based on the Cholesky decomposition of the environmental covariance matrix of the same population. The environmental component was partitioned into a permanent and a daily temporary effect. The combined effect of TBV and the environmental component was converted into an effect on the phenotypic performance of each animal for 6 of the 12 traits, for example effects on milk production, fertility, and risk of culling. Hence, genetics and phenotypic performance were associated. Estimated breeding values (EBV) were calculated using a normal inverse function based on correlated random numbers, the animal’s TBV and a standard deviation depending on the reliability of the estimate. The EBV were updated 3 times a year, with reliabilities depending on the age of animals. Complete technical and economic measures were calculated by the model over a period of time. Preliminary validation resulted in similar genetic changes per decade as predicted by USDA-AGIL when using the Net Merit index. The model is suitable to estimate the economic and genetic effects from using different reproductive strategies in dairy herds.