There are rather few articles in the literature so far that deal with dynamic multi-objective optimization problems. This article introduces a dynamic orthogonal multi-objective evolutionary algorithm called "DOMOEA", that generalizes an earlier paper of ours (on an orthogonal multi-objective evolutionary algorithm (OMOEA-II) ) to dynamic environments. DOMOEA solves a particular class of dynamic multi-objective optimization problems, namely those that have continuous decision variables. This new algorithm uses the evolutionary results, before any environmental change, as the initial population after the environmental change. It applies an "orthogonal design method" to enhance the fitness of the population during the static stages between two successive changes of environment. We obtained satisfactory results when testing this algorithm against the benchmark problems proposed in the literature . Our new algorithm is based on an ordinary evolutionary algorithm that does not have the capacity to detect environmental changes. Hence it has a comparatively simple structure, making comparisons with other dynamic multi-objective evolutionary algorithms relatively easy.