Computer simulations are an important tool for the current research in population and evolutionary genetics. They help to understand the genetic evolution of complex processes dynamics that cannot be analytically predicted. The basic idea is to generate synthetic data sets of genetic polymorphisms under user-specified scenarios describing the evolutionary history and genetic architecture of a species. In this work, we focus on forward-in-time simulations which represent the most powerful, but, at the same time, most compute-intensive approach for simulating the genetic material of a population. We present a highly-optimized forward-in-time simulation library called Libgdrift, specially designed to create large sets of replicated simulations. Our simulation library uses code optimizations such as spatial locality and a two-phase data compression approach which allow fast simulation executions, while reducing memory storage. Results show that our proposal can improve the performance reported by well-known simulation software.