The Willow Catkin Optimization (WCO) algorithm combines the characteristics of the dispersal process of willow catkin seeds with the optimization process of the algorithm. Due to its unique optimization principles, it has been proved to outperform some traditional optimization algorithms. However, it still has drawbacks such as low solution accuracy and poor local search capability. To address these deficiencies of the WCO algorithm, this article proposes a Multigroup and Multistrategy Willow Catkin Optimization algorithm (MMWCO). The MMWCO algorithm integrates the Genetic Algorithm (GA) in parallel into the Willow Catkin Optimization (WCO) framework. The algorithm’s optimization performance was tested using 30 different types of test functions from CEC2017. Numerical experiment results demonstrate that the MMWCO algorithm outperforms the original WCO algorithm and also shows advantages compared to other intelligent algorithms. Finally, this method was applied to the vehicle routing problem with time windows in transportation. Using Solomon’s standard test data, the algorithm achieved favorable results, proving its practicality and feasibility.