This paper presents a decision support framework for using autonomous tuning algorithms in swarm robotics applications. With the rapid emergence of swarm robotics, there is a growing need to automate parameter tuning for efficient transfer to diverse robotic platforms. Simulation-optimisation frameworks address this, but a key tradeoff exists between tuning speed and parameter transferability. This tradeoff is investigated using the Frontier-Led Swarming Simulation Optimisation Framework (FLS-SOF), and two simulation models are compared: a lightweight point-mass model and a realistic differential-drive model. Experiments span three tasks–coverage, navigation, and cooperative maze solving–executed across ground and aerial robot platforms. Results show that while the point-mass simulator enables a 5.4× speedup in optimisation and may be suitable for early-stage exploration or time-constrained development cycles, its tuned parameters exhibit limited transferability to complex tasks, incurring up to 98.7% more collisions. In contrast, the differential-drive simulator improves navigation success by 196.7% and maze exit rates by 125%, while maintaining safety and coordination. These insights are synthesised into a flowchart-based decision tool that guides simulator selection based on task complexity, platform dynamics, and performance objectives. This tool enables practitioners to make fidelity-aware choices for optimising transferable swarm behaviours in real-world robotics deployments.