Computational simulation of physical phenomena plays a central role in many important applications, including scientific visualization and the generation visual effects for entertainment. Typically, these simulations rely on high-quality meshes to model physical objects. Meshes with badly shaped elements degrade both the accuracy and efficiency of the simulation. Traditionally, mesh optimization has relied on global algorithms which are ill-suited to the massive meshes demanded by many modern applications. In this paper, we describe a streaming framework for tetrahedral mesh optimization. We provide empirical results demonstrating that streaming is faster and more memory efficient than global optimization while resulting in essentially identical mesh quality. We also describe a novel streaming method for optimizing the surface of a tetrahedral mesh that is efficient, preserves features, and significantly increases the tetrahedral mesh quality.