Restoring power distribution networks after disruptions demands rapid, reliable coordination across repair crews, mobile power sources, and switching actions under strict constraints. Classical optimization yields high-quality plans but can be slow, while reinforcement learning often requires feeder-specific training and careful reward shaping. We recast restoration as language-conditioned planning: a large language model generates high-level restoration plans over a compact pre-validated catalogue of feasible actions. This constrained generation design makes decisions reliably, scalably, and interpretably, and allows for real-time human-in-the-loop decision-making while requiring no topology-specific setup or retraining. Our method achieves near-mixed-integer-linear programming performance on the IEEE 13-node standard power distribution feeder and outperforms a time-capped MILP solver on the IEEE 33-node standard feeder by around 13%, while using less than 1% of its wall-clock runtime.