Fishery-independent surveys are essential for collecting high-quality data to support the stock assessments and management of regular fisheries. In general, such programs are more costly and time-consuming than commercial fishery-dependent programs. Thus, considerable interest exists in using computer simulations to optimize methods for obtaining high-quality data with limited sampling effort. Currently, high intensity fishery-independent bottom trawl surveys may negatively affect and disturb fish populations and the ecosystem of fragile estuarine habitats. These areas support many important fishery species; however, they are also among the most extensively affected and threatened aquatic environments due to fishing pressure and environmental stressors such as coastal development. In this study, we developed computer simulations to evaluate and optimize sampling of mean body length and weight of target fish species in a cluster sampling survey. For use in simulations, bottom trawl surveys were conducted in the Yellow River estuary and its adjacent waters during 2013 (August, October) and 2014 (February, May) to collect abundance and biological-trait data on red tongue sole (). The relative estimation error (REE), relative bias (RB), and coefficient of variation (CV) were used to measure the performance (accuracy, precision, and efficiency) of sampling schemes. These indices increased for simulated data when the number of sampling sections decreased. In the current survey design, a reduction in sampling-section number from five to three would reduce sampling effort by 40%, while increasing REE by only~2% in about 40% of the catches. Thus, three sections are acceptable for surveys designed to obtain size-based indicators. This study also showed that sampling-effort optimization may vary between different survey objectives. Therefore, a post-survey analysis will improve fishery-independent survey designs based on specific survey goals, thereby yielding more effective survey data.