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In order to improve the efficiency of information acquisition and task selection in Virtual Reality (VR) systems, enhance the interactive experience, and reduce cognitive load for users, it is crucial to effectively organize and leverage user cognitive psychology and design elements during the VR scene design phase. This paper focuses on analyzing the low cognitive load requirements of users and the need for a satisfactory user perceptual experience based on the cognitive resource theory. We propose a method for optimizing the design of VR system scenario tasks under low cognitive load requirements. By utilizing human-computer hybrid intelligent assistance for predicting user cognitive load and incorporating intelligent optimization genetic algorithms into the optimization of VR system design elements, we aim to minimize cognitive load as the objective function based on the principle of low cognitive load. Important knowledge granularity nodes are used as fitness functions in the optimization process of VR system design resource elements. An application study is conducted, combining the multi-channel cognition in a smart city VR system task information interface, to optimize the system resource features. The study validates and compares the solutions obtained through traditional design processes and the solutions optimized by the method proposed in this paper, using virtual reality eye-tracking experiments for the same design task requirements in VR systems. The results demonstrate that users experience lower cognitive load and better task operation experience when interacting with the optimized solutions proposed in this paper. Therefore, the optimization method studied in this paper can serve as a reference for the construction of virtual reality systems. [ABSTRACT FROM AUTHOR]