Invasive annual grasses alter fire regime in steppe ecosystems, and subsequent trends toward larger, more frequent wildfires impacts iconic biodiversity. A common solution is to disrupt novel fuel beds comprising continuous swaths of invasive annual grasses with greenstrips—linear, human-maintained stands of less-flammable vegetation. But selecting effective native species is challenged by the fact that identifying the optimal combination of plant traits that interrupt wildfire spread is logistically difficult. We employed fire behavior simulation modeling to determine plant traits with high potential to slow fire spread in annual Bromus-dominated fuelbeds. We found species with low leaf:stem (fine:coarse) ratios and high live:dead fuel ratios to be most effective. Our approach helps isolate fuelbed characteristics that slow fire spread, providing a geographically-agnostic framework to scale plant traits to greenstrip effectiveness. This framework helps managers assess potential native species for greenstrips without needing logistically-difficult experimental assessments to determine how a species might affect fire behavior.