With the increasing demand for multi-robot exploration of unknown environments, how to accomplish this problem efficiently has become a focus of research. However, in this kind of task, the formulation of strategies for frontier point detection and task allocation largely determines the overall efficiency of the system. In the task of multi-robot exploration of unknown environments, the strategies of frontier point detection and task assignment determine the overall efficiency of the system. Most of the existing methods implement frontier point detection based on the Rapidly-Exploring Random Tree (RRT) and use greedy algorithms for task allocation. However, the classical RRT algorithm is a fixed growth step, which leads to the difficulty of growing branches in narrow environments, making the efficiency and correctness of detecting frontier points lower. Meanwhile, the allocation strategy of the greedy algorithm causes each robot to consider only the exploration area with the largest gain for itself, which easily leads to repeated exploration and reduces the overall efficiency of the system. To solve these problems, we propose an adaptive RRT tree growth strategy for frontier point detection, which can adjust the step size according to the known map information and thus improve the efficiency and accuracy of detection; and introduce a Bayesian-guided evolutionary strategy(BGE) for efficient task allocation, which can utilize the current and historical information to find the optimal allocation scheme in a global perspective. We conduct a comprehensive test of the proposed strategy in the ROS system as well as in the real world, which proves the efficiency of our strategy. Our code is open-sourced and can be provided under request.