The automated techniques used to collect pavement conditions on county roads are relatively expensive for local agencies. This study evaluates the possibility of reducing the amount of pavement condition data collected in each survey to optimize the costs of data collection. This study applies multiple imputation analyses as an assistant tool to estimate the uncollected condition data at the network level. Another objective of this study is to determine the most cost-effective pavement condition data collection frequencies. By using a case study of secondary paved highways in Wyoming, it was concluded that uncollected condition indices can be predicted using the initial/historical values. The imputation models developed in this paper provide good estimations. Cost analysis of county roads for two counties demonstrates the significant amounts of cost saving when applying the proposed imputation strategy during data collection process. Therefore, pavement condition data is not recommended to be collected for the whole network annually on county roads. A less expensive sequence can be adopted instead where the data that is not collected can be predicted.