To optimize the task allocation problem of multi-construction robots, a mathematical model is established with the goal of minimizing the shortest robot travel distance. The robot's electric power is used as a constraint, and a genetic algorithm is employed to solve the model. To enhance the optimization capability and convergence speed of the algorithm, several strategies are implemented. First, the improved circle strategy optimizes the initial population. Then, the adaptive crossover mutation operator and secondary mutation are introduced to adjust individuals. Finally, the elite retention strategy ensures that excellent individuals are preserved.Task allocation simulation experiments were conducted under two groups with different numbers of robots and tasks. The simulation results show that the path length solved by the improved genetic algorithm is shortened by 3.7% and 12.5% compared with the basic genetic algorithm, verifying the effectiveness of the improved algorithm.