Performance evaluation of the front-line staff in an electric power company often encounters problems such as hard assessment for the raters and undue quantification. In response, this study proposes a model of performance evaluation based on work tickets. Vertical quantification of counting is performed for the same work item through simple characterization of multi-dimensional evaluation attributes, and horizontal clustering is conducted for different work items according to the weights of the subjective assessment by team leaders. In this way, the model tries to explore the hidden information in the raters’ subjective assessment. Meanwhile, a dynamic random population topology-particle swarm optimization (RPT-PSO) algorithm based on the average degree is proposed to solve the model. In-depth research is conducted on the encoding modes of particles, the way that constraints are handled, and the specific implementation of strategies in the algorithm. Finally, five teams of the same type are selected for performance measurement by the proposed model. The computational results demonstrate that the proposed model and the RPT-PSO algorithm are effective, providing a new solution to the performance evaluation of the front-line staff in an electric power company.