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The Multi-Objective Discrete Network Design Problem (MO-DNDP) describes the optimisation of a transport network across multiple metrics. Due to the impractical processing times of exact solutions, heuristics have been harnessed to solve this problem. However, the scale of the networks, the set of candidate links and the metrics assessed have been limited. This research proposes a MO-DNDP that encapsulates 8 metrics across three real networks used as benchmarks scaling from 17 km2 to 41,850 km2 to encapsulate the complexity of optimising transport networks at different scales, traffic demands and constraint densities. To solve this more comprehensive MO-DNDP, this paper proposes the Physarum-inspired algorithm PANDA that improves upon the classical Physarum Solver algorithm by harnessing the attractant sensing and merging capabilities of the biological Physarum Polycephalum slime mould. PANDA is a first attempt at solving the complex MO-DNDP using the Physarum foraging model. PANDA consistently scored better than the classical Solver in the land use rating by circumventing landforms, built-up areas, and environmentally sensitive regions. Additionally, PANDA constructed networks that were on average 30 % better than the existing benchmark networks and 32 % better than the Physarum Solver networks. PANDA improved upon the benchmarks by constructing lower capacity roads that were optimised for the actual traffic demand between each origin–destination pair instead of shared arterials. [ABSTRACT FROM AUTHOR]