This paper presents a multi-offspring genetic algorithm (MGA) with two-point crossover in accordance with biology and mathematical ecological theory. For the MGA, the main existing problems are generation methods of multi-offsprings with different crossover methods, the best number of offsprings and the influence of the number of offsprings on the speed of computation. To solve these problems, the paper first studies the relationship between the number of offsprings and the computational speed of the MGA with two-point crossover. Furthermore, the relationship between the generation method of multi-offsprings, the number of offsprings and the computational speed is analyzed. The results with ten test functions show that when the number of offsprings generated by the MGA based on two-point crossover equals 6, the MGA with two-point crossover has significantly improved the computational speed and reduced the number of iterations as compared to the basic genetic algorithm (BGA) and the MGA of single-point crossover.