This paper navigates the subject of “quality” in simulation from the lens of quality-improvement practices, analytically consolidated into a synthesis of associated practices. These practices were initially extracted from an extensive systematic search comprising agent-based models (ABM), discrete-event simulation (DES), system dynamics (SD) and modelling and simulation (M&S) papers, constructing a dataset of 491 practices (following refinement). Their utility was assessed from the perspective of co-occurrence. A synthesis of practices was then constructed through searching the co-occurrence graph, employing a genetic algorithm to maximise cross-relational popularity, while preserving all stages of the simulation lifecycle. The goal is to understand the most popular quality-improvement practices across all stages of simulation development, forming a multi-methodological view of quality. For new simulationists, this paper presents practices to prioritise in their learning to ensure simulation quality. For experts, it suggests how practices are associated through co-utilisation and occurrence, making an open call for improvement.