Automated Test Case Generation for Path Coverage （ATCG-PC） problem is a hot topic in the field of automated software testing. The fitness functions commonly used by swarm intelligence evolutionary algorithms in ATCG-PC problem are highly similar with each other， but the existing swarm intelligence evolutionary algorithms for solving ATCG-PC problem do not consider this similarity feature yet. Inspired by the similarity feature， the two similar fitness functions were treated as two tasks， so that ATCG-PC problem was transformed into a multi-task ATCG-PC problem， and a new swarm intelligence evolutionary algorithm called Multifactorial Backtracking Search optimization Algorithm （MFBSA） was proposed to solve multi-task ATCG-PC problem. In the proposed algorithm， the memory population function of multifactorial selection Ⅰ was used to improve the global search ability， and the similar tasks were able to improve each other’s optimization efficiency through knowledge transfer by assortative memory mating. The performance of MFBSA was evaluated on six fog computing test programs and six natural language processing test programs. Compared with Backtracking Search optimization Algorithm （BSA）， Immune Genetic Algorithm （IGA）， Particle Swarm Optimization with Convergence Speed Controller （PSO-CSC） algorithm， Adaptive Particle Swarm Optimization （APSO） algorithm and Differential Evolution with Hypercube-based learning strategies （DE-H） algorithm， MFBSA has the total test cases used to cover the paths on 12 test programs reduced by 64.46%， 66.64%， 67.99%， 74.15%， and 61.97%， respectively. Experimental results show that the proposed algorithm can effectively reduce testing cost.