Probability-based designs reduce bias and allow inference of results to the pool of sites from which they were chosen. We developed and tested probability-based designs for monitoring marine rocky intertidal assemblages at Glacier Bay National Park and Preserve (GLBA), Alaska. A multilevel design was used that varied in scale and inference. The levels included aerial surveys, extensive sampling of 25 sites, and more intensive sampling of 6 sites. Aerial surveys of a subset of intertidal habitat indicated that the original target habitat of bedrock-dominated sites with slope ≤30° was rare. This unexpected finding illustrated one value of probability-based surveys and led to a shift in the target habitat type to include steeper, more mixed rocky habitat. Subsequently, we evaluated the statistical power of different sampling methods and sampling strategies to detect changes in the abundances of the predominant sessile intertidal taxa: barnacles Balanomorpha, the mussel Mytilus trossulus, and the rockweed Fucus distichus subsp. evanescens. There was greatest power to detect trends in Mytilus and lesser power for barnacles and Fucus. Because of its greater power, the extensive, coarse-grained sampling scheme was adopted in subsequent years over the intensive, fine-grained scheme. The sampling attributes that had the largest effects on power included sampling of “vertical” line transects (vs. horizontal line transects or quadrats) and increasing the number of sites. We also evaluated the power of several management-set parameters. Given equal sampling effort, sampling more sites fewer times had greater power. The information gained through intertidal monitoring is likely to be useful in assessing changes due to climate, including ocean acidification; invasive species; trampling effects; and oil spills.