Measurement error of explanatory variables used in sightability models can result in biased population estimates and associated measures of precision. We developed a Monte Carlo simulation procedure that can be implemented within the sightability model framework when measurement error is present. Additionally, we developed simulation and sample survey methods, for determining the optimal allocation of survey effort to maximize precision of population estimates for a fixed survey cost, when a complete survey of a study area is not feasible. We used data from aerial surveys of elk during 2004–2006 in Michigan to demonstrate the application of these techniques. By accounting for measurement error and applying appropriate survey design practices, managers employing sightability models may be able to generate more accurate and cost-effective population estimates and accompanying measures of precision than is possible if these techniques are ignored.