Fisheries research surveys provide important information on the state of marine populations and ecosystems over time. Survey data are critical inputs to stock assessment, ecosystem-based fishery management initiatives, and applied ecological research. However, environmental changes may affect system stationarity, species distribution, and sampling effectiveness, so it can impact the consistency and trustfulness of abundance estimates time series computed from survey data. Therefore, it is essential to design flexible surveys that can adapt to climatic variability and budget limitations while keeping high-quality time series to effectively manage marine resources. To address these issues, we investigated multiple sampling survey designs in the Eastern Bering Sea because of the ongoing and rapid environmental change that is causing species distribution shifts to deeper and northern regions in the system. We used the eastern Bering Sea groundfish bottom trawl survey which is a systematic fishery-independent survey with a fixed station design and carried out since 1982. We conducted a multispecies survey design optimization using a spatiotemporal operating model and a genetic algorithm that optimizes the minimal optimal allocation of samples without compromising the precision of abundance estimates. Results revealed that reducing and redistributing samples and expanding the sampling range can provide accurate abundance estimates of marine populations under changing environmental conditions and budget limitations. This simulation study provided a framework that may help to increase the efficiency of sampling marine resources to maintain the quality of such time series of abundance estimates for the management of marine populations.