The growing threat of natural disasters poses major challenges for power system resilience, with wildfires from power line ignitions emerging as a particularly urgent concern. Utilities have increasingly relied on public safety power shutoff (PSPS) events to mitigate wildfire risk, but these actions create difficult tradeoffs between reducing ignition risk and maintaining reliable power delivery. This talk presents optimization-based approaches for transmission grid planning and operations that explicitly account for wildfire ignition risk mitigation. I develop models that integrate geospatial ignition risk data into optimal transmission switching as well as optimally plan and operate infrastructure investment options such as line undergrounding and long-duration batteries. To solve these Mixed Integer Programs (MIPs) in tractable time, I employ a number of computational strategies, leveraging stochastic programming–based decomposition methods, heuristics, and high-performance computing to model realistic transmission networks over long time horizons. Together, these methods provide a framework for informing policy and guiding resilient infrastructure investments that can enhance grid resilience, reduce wildfire risk, and support reliable service under normal and extreme conditions.