Wildlife conservation and management increasingly considers genetic information to plan, understand and evaluate implemented population interventions. These actions commonly include conservation translocation and population reductions through removals. Change in genetic variation in response to management actions can be unintuitive due to the influence of multiple interacting drivers (e.g. genetic drift, life history traits, environmental stochasticity). Simulation is an excellent tool to understand the predicted consequences of different proposed or implemented actions. However, the genetic simulators that are robust to a wide variety of life history traits also have a steep learning curve to appropriately parameterize common management actions. To fill this gap, we have developed cgsim, an R package for simulating the genetic consequences of common management interventions for populations of wildlife species. We developed a set of functions to specifically understand the effects of four main aspects of managing small, declining or isolated populations: loss of genetic diversity to drift, augmenting existing populations (e.g. translocation), population reduction through targeted removals and population catastrophes driven by stochastic extrinsic forces. Our single population simulation model is individual-based, and flexible to a range of life history traits. Here we validate cgsim through comparison of simulations to theoretical expectations of genetic diversity loss and illustrate its applied utility by focusing on a recently published empirical example for the Greater Sage-Grouse. Cgsim is available as an R package at: https://doi.org/10.5066/P1BXBEXJ.