A surrogate-assisted framework for the aerodynamic optimisation of distributed propulsion architectures is presented using the reformulated vortex particle method (rVPM) implemented in the open-source FLOWUnsteady solver. The approach targets early-stage design, enabling the prediction of unsteady propeller-wing interactions without the mesh-generation cost associated with conventional computational fluid dynamics (CFD) methods. The methodology is validated against experimental data for an isolated APC $10{\times }7$ propeller and against high-fidelity OpenFOAM simulations for a coupled wing–propeller configuration. Among the candidate layouts investigated, the tractor configuration with inboard-up propeller rotation provided the best aerodynamic performance and was selected as the baseline. Drag minimisation was then performed over an eight-variable constrained design space using Latin Hypercube Sampling and an Efficient Global Optimisation framework. The optimised configuration achieved a drag coefficient reduction from $151$ to $23.9$ counts, corresponding to an approximately $84\%$ decrease. Results demonstrate the effectiveness and computational efficiency of the proposed meshless optimisation workflow for distributed-propulsion aerodynamic design.