The ant colony-genetic optimization algorithm（ACGO） and genetic-ant colony optimization algorithm（GACO） interactive algorithms based on genetic algorithm and ant colony algorithm was proposed to optimize the path of sparse planting transplanting to solve the problem of low efficiency in planning the path of transplanting plug seedlings to low-density plug trays. The fixed sequence method and five other algorithms were used to calculate the length of transplanting path for plug trays with holes from 72-32，72-50，128-50， and 128-32 holes through simulation experiments. Comparative analyses were conducted on the performance of algorithms in optimizing the length of transplanting path and the time of calculation. The stability of the algorithm was evaluated by the relative standard deviation. The results showed that the GACO algorithm reduced the average length of path in transplanting plug tray with 72 to 32 holes by 59.3% compared with the fixed sequence method， with an average time of calculation of 5.15 seconds and a relative standard deviation of approximately 1.5%. The ACGO algorithm reduced the average length of path by 19.2%， with an average time of calculation of 13.50 seconds and a relative standard deviation of approximately 1%. The results of further studies showed that the optimization effect of ACGO algorithm in the scenarios of transplanting from 200 holes to 72 holes and 105 holes was weaker than that of greedy algorithm， while GACO algorithm had higher universality and stability under different combinations of hole numbers and numbers of missed seedling. It is indicated that both interactive algorithms have improved the performance of the original algorithm， but the GACO algorithm performs better in dealing with problems in planning path of complex sparse planting and transplanting plug seedlings. It will provide a strong reference basis for optimizing the path of sparse planting and transplanting plug seedlings.