Level of service (LoS) is a standard indicator, widely used in crowd management and urban design, for characterizing the service afforded by environments to crowds of specific densities. However, current LoS indicators are qualitative and rely on expert analysis. Computational approaches for crowd analysis and environment design require robust measures for characterizing the relationship between environments and crowd flow. In this paper, the flow-density relationships of environments optimized for flow under various LoS conditions are explored with respect to three state-of-the-art steering algorithms. We optimize environment elements to maximize crowd flow under a range of density conditions corresponding to common LoS categories. We perform an analysis of crowd flow under LoS conditions corresponding to the LoS optimized environments. We then perform an analysis of the crowd flow for these LoS optimized environments across LoS conditions. The steering algorithm, the number of optimized environment elements, the scenario configuration and the LoS conditions affect the optimal configuration of environment elements. We observe that the critical density of crowd simulators can increase, or shift LoS, due to the optimal placement of pillars. Depending on the steering model and environment benchmark, pillars are configured to produce lanes or form wall-like structures, in an effort to maximize crowd flow. These experiments serve as a precursor to environment optimization and crowd management motivating the need for further study using real and synthetic crowd datasets across a larger representation of environments.