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With the continuous development of social economy, engineering management technology is becoming increasingly mature, and multi-objective optimization technology is widely used in engineering management. Based on the traditional schedule, cost and quality model, the article introduces the environmental factor as the fourth optimization objective and constructs a comprehensive four-objective optimization model. The model starts from the concept of environmental protection, and proposes the modeling method of ecological indicators for deterministic and non-deterministic conditions. The article utilizes the multi-objective comprehensive optimization model to conduct an in-depth analysis of the engineering management condition of S project. After the engineering management optimization implementation, the optimization scheme showed that the planned duration was shortened to 324 days, which saved 12.9% of the construction time compared to the initial scheme and 1.81 million RMB in cost. In addition, the project achieved an average value of 0.9167 for quality reliability and a reduction in environmental impact to 296.48, which met the expected criteria in the S-engineering contract. The research in this paper not only provides a new type of multi-objective optimization method integrating the concept of environmental protection for project management, but also proves the effectiveness and practicability of this method in actual projects through example analysis.

Small construction objects are often built by standard task teams. The problem is, how to allocate these teams to individual works? To solve the problem of allocation three methods have been developed. The first method allows to designate optimal allocation of teams to the individual works in deterministic conditions of implementation. As a criterion of the optimal allocation can be applied: “the minimization of time” or “the minimization of costs” of works execution. The second method has been developed analogously for both criteria but for stochastic conditions and for the stochastic data. The third method allows to appoint a compromise allocation of teams. In this case, the criteria “the minimization of time” and “the minimization of costs” are considered simultaneously. The method can be applied in deterministic or stochastic conditions of works implementation. The solutions of the allocation problems which have been described allow to designate the optimal allocation of task teams and to determine the schedule and cost of works execution.