During the inter-class test, a common problem, named Class Integration and Test Order (CITO) problem, involves the determination of a test class order that minimizes stub creation effort, and consequently test costs. The approach based on Multi-Objective Evolutionary Algorithms (MOEAs) has achieved promising results because it allows the use of different factors and measures that can affect the stubbing process. Many times these factors are in conflict and usually there is no a single solution for the problem. Existing works on MOEAs present some limitations. The approach was evaluated with only two coupling measures, based on the number of attributes and methods of the stubs to be created. Other MOEAs can be explored and also other coupling measures. Considering this fact, this paper investigates the performance of two evolutionary algorithms: NSGA-II and SPEA2, for the CITO problem with four coupling measures (objectives) related to: attributes, methods, number of distinct return types and distinct parameter types. An experimental study was performed with four real systems developed in Java. The obtained results point out that the MOEAs can be efficiently used to solve this problem with several objectives, achieving solutions with balanced compromise between the measures, and of minimal effort to test.