At the cellular scale, many biological processes are driven by random interactions between molecules present in small populations. These systems are inherently stochastic and often display behavior that cannot be represented with traditional deterministic approaches. Gillespie\'s Stochastic Simulation Algorithm (SSA) provides a method for simulating spatially homogeneous biochemical systems in a way that captures this randomness. Since Gillespie\'s pioneering work, there have been several advances in efficient exact and approximate variants of the SSA. In this talk, I will provide an overview of the SSA and discuss StochKit2, a software package that provides efficient implementations of several of the most mature SSA variants. Then I will present my recent work on the development of a multiscale SSA implementation that exploits the stochastic partial equilibrium and quasi steady-state approximations.