The convergence and the diversity are two main goals of an evolutionary algorithm for many-objective optimization problems. However, achieving these two goals simultaneously is the difficult and challenging work for multi-objective evolutionary algorithms. A uniform evolutionary algorithm based on decomposition and the control of dominance area of solutions (CDAS) is proposed to achieve these two goals. Firstly, a uniform design method is utilized to generate the weight vectors whose distribution is uniform over the design space, then the initial population is classified into some sub-populations by these weight vectors. Secondly, an update strategy based on decomposition is proposed to maintain the diversity of obtained solutions. Thirdly, to improve the convergence, a crossover operator based on the uniform design method is constructed to enhance the search capacity and the CDAS is used to sort solutions of each sub-population to guide the search process to converge the Pareto optimal solutions. Moreover, the proposed algorithm compare with some efficient state-of-the-art algorithms, e.g., NSGAII-CDAS, MOEA/D, UMOEND and HypE, on six benchmark functions with 5-25 objectives are made, and the results indicate that the proposed algorithm is able to obtain solutions with better convergence and diversity.