Enhancing the detection rate of Wi-Fi Direct networks is essential for assessing network performance in practical applications. This research introduces the EOST (Environmental Optimization for Sniffing Time) algorithm, a novel detection approach for Wi-Fi Direct networks, which dynamically adjusts the sniffing window size for each channel by analyzing environmental characteristics such as the quantity of frames, frame size, and retransmission conditions. Experimental outcomes demonstrate that our algorithm significantly outperforms conventional frequency-hopping listening strategies, achieving a 20.55% improvement in device discovery rates and a 12.01% increase in link discovery rates.